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.gitattributes

@@ -33,3 +33,14 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+medpy/source/notebooks/scripts/images/adc.png filter=lfs diff=lfs merge=lfs -text
+medpy/source/notebooks/scripts/images/b0.png filter=lfs diff=lfs merge=lfs -text
+medpy/source/notebooks/scripts/images/b0gclabelstawiaski.png filter=lfs diff=lfs merge=lfs -text
+medpy/source/notebooks/scripts/images/b0gcvoxelmax.png filter=lfs diff=lfs merge=lfs -text
+medpy/source/notebooks/scripts/images/b0gradient.png filter=lfs diff=lfs merge=lfs -text
+medpy/source/notebooks/scripts/images/b0wmarker.png filter=lfs diff=lfs merge=lfs -text
+medpy/source/notebooks/scripts/images/b1000.png filter=lfs diff=lfs merge=lfs -text
+medpy/source/notebooks/scripts/images/graphcut_voxel_gradient.png filter=lfs diff=lfs merge=lfs -text
+medpy/source/notebooks/scripts/images/graphcut_voxel_grayvalues.png filter=lfs diff=lfs merge=lfs -text
+medpy/source/notebooks/scripts/images/watershed_colored.png filter=lfs diff=lfs merge=lfs -text
+medpy/source/notebooks/scripts/images/watershed.png filter=lfs diff=lfs merge=lfs -text

medpy/mcp_output/README_MCP.md

@@ -0,0 +1,107 @@
+# MedPy Plugin
+
+## Overview
+
+MedPy is a comprehensive library designed for medical image processing. It provides a wide range of tools and functionalities to facilitate the analysis and manipulation of medical images. The library supports various image formats and offers numerous utilities for image filtering, segmentation, and feature extraction.
+
+The repository for MedPy can be found at [GitHub - MedPy](https://github.com/loli/medpy).
+
+## Installation
+
+To install MedPy, ensure you have Python installed on your system. You can then clone the repository and install the package using the following commands:
+
+```bash
+git clone https://github.com/loli/medpy.git
+cd medpy
+python setup.py install
+```
+
+### Dependencies
+
+MedPy requires the following Python packages:
+
+- `numpy`
+- `scipy`
+- `matplotlib`
+
+Optional dependency:
+
+- `pydicom` (for handling DICOM files)
+
+## Usage
+
+MedPy provides several command-line interface (CLI) tools for various image processing tasks. Below are some of the available tools and their descriptions:
+
+### CLI Tools
+
+- **medpy_anisotropic_diffusion**: Performs anisotropic diffusion on images.
+- **medpy_convert**: Converts image formats.
+
+To use a CLI tool, run the following command in your terminal:
+
+```bash
+python -m bin.<tool_name> [options]
+```
+
+Replace `<tool_name>` with the desired tool, such as `medpy_anisotropic_diffusion`.
+
+## Available Tool Endpoints
+
+MedPy includes a variety of tools for different image processing needs. Here is a list of available tools:
+
+- `medpy_anisotropic_diffusion.py`
+- `medpy_apparent_diffusion_coefficient.py`
+- `medpy_binary_resampling.py`
+- `medpy_convert.py`
+- `medpy_create_empty_volume_by_example.py`
+- `medpy_dicom_slices_to_volume.py`
+- `medpy_dicom_to_4D.py`
+- `medpy_diff.py`
+- `medpy_extract_contour.py`
+- `medpy_extract_min_max.py`
+- `medpy_extract_sub_volume.py`
+- `medpy_extract_sub_volume_auto.py`
+- `medpy_extract_sub_volume_by_example.py`
+- `medpy_fit_into_shape.py`
+- `medpy_gradient.py`
+- `medpy_graphcut_label.py`
+- `medpy_graphcut_label_bgreduced.py`
+- `medpy_graphcut_label_w_regional.py`
+- `medpy_graphcut_label_wsplit.py`
+- `medpy_graphcut_voxel.py`
+- `medpy_grid.py`
+- `medpy_info.py`
+- `medpy_intensity_range_standardization.py`
+- `medpy_intersection.py`
+- `medpy_join_masks.py`
+- `medpy_join_xd_to_xplus1d.py`
+- `medpy_label_count.py`
+- `medpy_label_fit_to_mask.py`
+- `medpy_label_superimposition.py`
+- `medpy_merge.py`
+- `medpy_morphology.py`
+- `medpy_resample.py`
+- `medpy_reslice_3d_to_4d.py`
+- `medpy_set_pixel_spacing.py`
+- `medpy_shrink_image.py`
+- `medpy_split_xd_to_xminus1d.py`
+- `medpy_stack_sub_volumes.py`
+- `medpy_swap_dimensions.py`
+- `medpy_watershed.py`
+- `medpy_zoom_image.py`
+
+## Notes and Troubleshooting
+
+- Ensure all dependencies are installed before running the tools.
+- If you encounter issues with DICOM files, verify that `pydicom` is installed.
+- For detailed usage of each tool, refer to the help command:
+
+  ```bash
+  python -m bin.<tool_name> --help
+  ```
+
+- If you experience any issues or have questions, please refer to the [GitHub repository](https://github.com/loli/medpy) for further documentation and support.
+
+## License
+
+MedPy is licensed under the MIT License. For more details, see the `LICENSE.txt` file in the repository.

medpy/mcp_output/analysis.json

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+      }
+    },
+    "processed_by": "zip_fallback",
+    "success": true
+  },
+  "structure": {
+    "packages": [
+      "source.medpy",
+      "source.medpy.core",
+      "source.medpy.features",
+      "source.medpy.filter",
+      "source.medpy.graphcut",
+      "source.medpy.io",
+      "source.medpy.iterators",
+      "source.medpy.metric",
+      "source.medpy.neighbours",
+      "source.medpy.utilities",
+      "source.tests",
+      "source.tests.features_",
+      "source.tests.filter_",
+      "source.tests.graphcut_",
+      "source.tests.io_",
+      "source.tests.metric_"
+    ]
+  },
+  "dependencies": {
+    "has_environment_yml": false,
+    "has_requirements_txt": false,
+    "pyproject": false,
+    "setup_cfg": false,
+    "setup_py": true
+  },
+  "entry_points": {
+    "imports": [],
+    "cli": [],
+    "modules": []
+  },
+  "llm_analysis": {
+    "core_modules": [
+      {
+        "package": "source.medpy.core",
+        "module": "exceptions",
+        "functions": [],
+        "classes": [
+          "MedPyError",
+          "MedPyWarning"
+        ],
+        "description": "Defines core exceptions and warnings for MedPy."
+      },
+      {
+        "package": "source.medpy.features",
+        "module": "histogram",
+        "functions": [
+          "calculate_histogram",
+          "normalize_histogram"
+        ],
+        "classes": [],
+        "description": "Provides functions to calculate and normalize histograms."
+      },
+      {
+        "package": "source.medpy.filter",
+        "module": "image",
+        "functions": [
+          "anisotropic_diffusion",
+          "gaussian_filter"
+        ],
+        "classes": [],
+        "description": "Contains image filtering functions including diffusion and Gaussian filters."
+      },
+      {
+        "package": "source.medpy.graphcut",
+        "module": "graph",
+        "functions": [
+          "create_graph",
+          "cut_graph"
+        ],
+        "classes": [],
+        "description": "Implements graph-based segmentation methods."
+      },
+      {
+        "package": "source.medpy.io",
+        "module": "load",
+        "functions": [
+          "load_image"
+        ],
+        "classes": [],
+        "description": "Handles loading of medical images."
+      }
+    ],
+    "cli_commands": [
+      {
+        "name": "medpy_anisotropic_diffusion",
+        "module": "bin.medpy_anisotropic_diffusion",
+        "description": "CLI tool for performing anisotropic diffusion on images."
+      },
+      {
+        "name": "medpy_convert",
+        "module": "bin.medpy_convert",
+        "description": "CLI tool for converting image formats."
+      }
+    ],
+    "import_strategy": {
+      "primary": "import",
+      "fallback": "cli",
+      "confidence": 0.85
+    },
+    "dependencies": {
+      "required": [
+        "numpy",
+        "scipy",
+        "matplotlib"
+      ],
+      "optional": [
+        "pydicom"
+      ]
+    },
+    "risk_assessment": {
+      "import_feasibility": 0.8,
+      "intrusiveness_risk": "medium",
+      "complexity": "medium"
+    }
+  },
+  "deepwiki_analysis": {
+    "repo_url": "https://github.com/loli/medpy",
+    "repo_name": "medpy",
+    "content": null,
+    "model": "gpt-4o",
+    "source": "selenium",
+    "success": true
+  },
+  "deepwiki_options": {
+    "enabled": true,
+    "model": "gpt-4o"
+  },
+  "risk": {
+    "import_feasibility": 0.8,
+    "intrusiveness_risk": "medium",
+    "complexity": "medium"
+  }
+}

medpy/mcp_output/env_info.json

@@ -0,0 +1,15 @@
+{
+  "environment": {
+    "type": "conda",
+    "name": "medpy_136254_env",
+    "files": {},
+    "python": "3.10",
+    "exec_prefix": []
+  },
+  "original_tests": {
+    "passed": true,
+    "report_path": null
+  },
+  "timestamp": 1761136573.9414783,
+  "conda_available": true
+}

medpy/mcp_output/mcp_logs/llm_statistics.json

@@ -0,0 +1,11 @@
+{
+  "total_calls": 5,
+  "failed_calls": 0,
+  "retry_count": 0,
+  "total_prompt_tokens": 21657,
+  "total_completion_tokens": 5063,
+  "total_tokens": 26720,
+  "average_prompt_tokens": 4331.4,
+  "average_completion_tokens": 1012.6,
+  "average_tokens": 5344.0
+}

medpy/mcp_output/mcp_logs/run_log.json

@@ -0,0 +1,65 @@
+{
+  "timestamp": 1761136873.6488183,
+  "node": "RunNode",
+  "test_result": {
+    "passed": false,
+    "report_path": null,
+    "stdout": "",
+    "stderr": "ERROR conda.cli.main_run:execute(41): `conda run python mcp_output/start_mcp.py` failed. (See above for error)\nTraceback (most recent call last):\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/mcp_output/start_mcp.py\", line 17, in <module>\n    from mcp_service import create_app\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/mcp_output/mcp_plugin/mcp_service.py\", line 13, in <module>\n    from medpy.features.histogram import (\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/source/medpy/features/__init__.py\", line 154, in <module>\n    from .histogram import fuzzy_histogram as fuzzy_histogram\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/source/medpy/features/histogram.py\", line 25, in <module>\n    import numpy\nModuleNotFoundError: No module named 'numpy'\n\n"
+  },
+  "run_result": {
+    "success": false,
+    "test_passed": false,
+    "exit_code": 1,
+    "stdout": "",
+    "stderr": "ERROR conda.cli.main_run:execute(41): `conda run python mcp_output/start_mcp.py` failed. (See above for error)\nTraceback (most recent call last):\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/mcp_output/start_mcp.py\", line 17, in <module>\n    from mcp_service import create_app\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/mcp_output/mcp_plugin/mcp_service.py\", line 13, in <module>\n    from medpy.features.histogram import (\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/source/medpy/features/__init__.py\", line 154, in <module>\n    from .histogram import fuzzy_histogram as fuzzy_histogram\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/source/medpy/features/histogram.py\", line 25, in <module>\n    import numpy\nModuleNotFoundError: No module named 'numpy'\n\n",
+    "timestamp": 1761136873.6488087,
+    "error_type": "ImportError",
+    "error": "Module import failed: ERROR conda.cli.main_run:execute(41): `conda run python mcp_output/start_mcp.py` failed. (See above for error)\nTraceback (most recent call last):\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/mcp_output/start_mcp.py\", line 17, in <module>\n    from mcp_service import create_app\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/mcp_output/mcp_plugin/mcp_service.py\", line 13, in <module>\n    from medpy.features.histogram import (\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/source/medpy/features/__init__.py\", line 154, in <module>\n    from .histogram import fuzzy_histogram as fuzzy_histogram\n  File \"/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/source/medpy/features/histogram.py\", line 25, in <module>\n    import numpy\nModuleNotFoundError: No module named 'numpy'\n\n",
+    "details": {
+      "command": "/home/wshiah/code/miniconda3/bin/conda run -n medpy_136254_env --cwd /export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy python mcp_output/start_mcp.py",
+      "working_directory": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy",
+      "environment_type": "conda"
+    }
+  },
+  "environment": {
+    "type": "conda",
+    "name": "medpy_136254_env",
+    "files": {},
+    "python": "3.10",
+    "exec_prefix": []
+  },
+  "plugin_info": {
+    "files": {
+      "mcp_output/start_mcp.py": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/mcp_output/start_mcp.py",
+      "mcp_output/mcp_plugin/__init__.py": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/mcp_output/mcp_plugin/__init__.py",
+      "mcp_output/mcp_plugin/mcp_service.py": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/mcp_output/mcp_plugin/mcp_service.py",
+      "mcp_output/mcp_plugin/adapter.py": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/mcp_output/mcp_plugin/adapter.py",
+      "mcp_output/mcp_plugin/main.py": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/mcp_output/mcp_plugin/main.py",
+      "mcp_output/requirements.txt": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/mcp_output/requirements.txt",
+      "mcp_output/README_MCP.md": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/mcp_output/README_MCP.md",
+      "mcp_output/tests_mcp/test_mcp_basic.py": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/mcp_output/tests_mcp/test_mcp_basic.py"
+    },
+    "adapter_mode": "import",
+    "endpoints": [
+      "medpyerror",
+      "medpywarning",
+      "calculate_histogram",
+      "normalize_histogram",
+      "anisotropic_diffusion",
+      "gaussian_filter",
+      "create_graph",
+      "cut_graph",
+      "load_image"
+    ],
+    "mcp_dir": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/mcp_output/mcp_plugin",
+    "tests_dir": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/mcp_output/tests_mcp",
+    "main_entry": "start_mcp.py",
+    "readme_path": "/export/project/shiweijie/ghh/LLM_MCP_RAG/MCP-agent-github-repo-output/workspace/medpy/mcp_output/README_MCP.md",
+    "requirements": [
+      "fastmcp>=0.1.0",
+      "pydantic>=2.0.0"
+    ]
+  },
+  "fastmcp_installed": false
+}

medpy/mcp_output/mcp_plugin/adapter.py

@@ -0,0 +1,117 @@
+import os
+import sys
+
+# Path settings
+source_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), "source")
+sys.path.insert(0, source_path)
+
+# Import statements
+try:
+    from medpy.features.histogram import Histogram
+    from medpy.features.intensity import Intensity
+    from medpy.filter.image import ImageFilter
+    from medpy.io.load import load
+    from medpy.io.save import save
+except ImportError as e:
+    print(f"Import error: {e}. Some functionalities may not be available.")
+
+class Adapter:
+    """
+    Adapter class for the MCP plugin, providing access to various functionalities
+    from the medpy library.
+    """
+    
+    def __init__(self):
+        self.mode = "import"
+    
+    # -------------------------------------------------------------------------
+    # Feature Extraction Methods
+    # -------------------------------------------------------------------------
+    
+    def create_histogram(self, image_data):
+        """
+        Create a histogram from the given image data.
+
+        :param image_data: The image data to process.
+        :return: A dictionary with the status and histogram data.
+        """
+        try:
+            histogram = Histogram(image_data)
+            return {"status": "success", "histogram": histogram}
+        except Exception as e:
+            return {"status": "error", "message": str(e)}
+    
+    def calculate_intensity(self, image_data):
+        """
+        Calculate intensity features from the given image data.
+
+        :param image_data: The image data to process.
+        :return: A dictionary with the status and intensity data.
+        """
+        try:
+            intensity = Intensity(image_data)
+            return {"status": "success", "intensity": intensity}
+        except Exception as e:
+            return {"status": "error", "message": str(e)}
+    
+    # -------------------------------------------------------------------------
+    # Image Filtering Methods
+    # -------------------------------------------------------------------------
+    
+    def apply_image_filter(self, image_data, filter_type):
+        """
+        Apply a specified filter to the image data.
+
+        :param image_data: The image data to filter.
+        :param filter_type: The type of filter to apply.
+        :return: A dictionary with the status and filtered image data.
+        """
+        try:
+            image_filter = ImageFilter()
+            filtered_image = image_filter.apply_filter(image_data, filter_type)
+            return {"status": "success", "filtered_image": filtered_image}
+        except Exception as e:
+            return {"status": "error", "message": str(e)}
+    
+    # -------------------------------------------------------------------------
+    # IO Methods
+    # -------------------------------------------------------------------------
+    
+    def load_image(self, file_path):
+        """
+        Load an image from the specified file path.
+
+        :param file_path: The path to the image file.
+        :return: A dictionary with the status and loaded image data.
+        """
+        try:
+            image_data, header = load(file_path)
+            return {"status": "success", "image_data": image_data, "header": header}
+        except Exception as e:
+            return {"status": "error", "message": str(e)}
+    
+    def save_image(self, image_data, file_path):
+        """
+        Save the image data to the specified file path.
+
+        :param image_data: The image data to save.
+        :param file_path: The path to save the image file.
+        :return: A dictionary with the status of the save operation.
+        """
+        try:
+            save(image_data, file_path)
+            return {"status": "success", "message": "Image saved successfully."}
+        except Exception as e:
+            return {"status": "error", "message": str(e)}
+    
+    # -------------------------------------------------------------------------
+    # Fallback Handling
+    # -------------------------------------------------------------------------
+    
+    def fallback_mode(self):
+        """
+        Handle operations in fallback mode when imports fail.
+
+        :return: A dictionary with the status and message.
+        """
+        return {"status": "warning", "message": "Running in fallback mode. Some functionalities are limited."}

medpy/mcp_output/mcp_plugin/main.py

@@ -0,0 +1,13 @@
+"""
+MCP Service Auto-Wrapper - Auto-generated
+"""
+from mcp_service import create_app
+
+def main():
+    """Main entry point"""
+    app = create_app()
+    return app
+
+if __name__ == "__main__":
+    app = main()
+    app.run()

medpy/mcp_output/mcp_plugin/mcp_service.py

@@ -0,0 +1,617 @@
+import os
+import sys
+import numpy as np
+import json
+
+source_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), "source")
+sys.path.insert(0, source_path)
+
+from fastmcp import FastMCP
+from medpy.core.exceptions import (
+    ArgumentError, DependencyError, FunctionError, ImageLoadingError,
+    ImageSavingError, ImageTypeError, MetaDataError, SubprocessError
+)
+from medpy.core.logger import Logger
+from medpy.features.histogram import (
+    fuzzy_histogram, gaussian_membership, sigmoidal_difference_membership,
+    trapezoid_membership, triangular_membership
+)
+from medpy.features.intensity import (
+    centerdistance, centerdistance_xdminus1, gaussian_gradient_magnitude,
+    hemispheric_difference, indices, intensities, local_histogram,
+    local_mean_gauss, mask_distance, median, shifted_mean_gauss
+)
+from medpy.features.utilities import append, join, normalize, normalize_with_model
+from medpy.filter.IntensityRangeStandardization import (
+    InformationLossException, IntensityRangeStandardization,
+    SingleIntensityAccumulationError, UntrainedException
+)
+from medpy.filter.binary import bounding_box, largest_connected_component, size_threshold
+from medpy.filter.houghtransform import ght, ght_alternative, template_ellipsoid, template_sphere
+from medpy.filter.image import (
+    average_filter, local_minima, otsu, resample, sls, ssd, sum_filter
+)
+from medpy.filter.label import fit_labels_to_mask, relabel, relabel_map, relabel_non_zero
+from medpy.filter.noise import immerkaer, immerkaer_local, separable_convolution
+from medpy.filter.smoothing import anisotropic_diffusion, gauss_xminus1d
+from medpy.filter.utilities import intersection, pad, xminus1d
+from medpy.graphcut.energy_label import (
+    boundary_difference_of_means, boundary_stawiaski, boundary_stawiaski_directed,
+    regional_atlas
+)
+from medpy.graphcut.energy_voxel import (
+    boundary_difference_division, boundary_difference_exponential,
+    boundary_difference_linear, boundary_difference_power, boundary_maximum_division,
+    boundary_maximum_exponential, boundary_maximum_linear, boundary_maximum_power,
+    regional_probability_map
+)
+from medpy.graphcut.generate import graph_from_labels, graph_from_voxels
+from medpy.graphcut.graph import GCGraph, Graph
+from medpy.graphcut.wrapper import graphcut_split, graphcut_stawiaski, graphcut_subprocesses, split_marker
+from medpy.graphcut.write import graph_to_dimacs
+from medpy.io.header import (
+    Header, copy_meta_data, get_offset, get_pixel_spacing, get_voxel_spacing,
+    set_offset, set_pixel_spacing, set_voxel_spacing
+)
+from medpy.io.load import load
+from medpy.io.save import save
+from medpy.iterators.patchwise import CentredPatchIterator, CentredPatchIteratorOverlapping, SlidingWindowIterator
+from medpy.metric.binary import (
+    asd, assd, dc, hd, hd95, jc, obj_asd, obj_assd, obj_fpr, obj_tpr,
+    positive_predictive_value, precision, ravd, recall, sensitivity, specificity,
+    true_negative_rate, true_positive_rate, volume_change_correlation, volume_correlation
+)
+from medpy.metric.histogram import (
+    chebyshev, chebyshev_neg, chi_square, correlate, correlate_1, cosine, cosine_1,
+    cosine_2, cosine_alt, euclidean, fidelity_based, histogram_intersection,
+    histogram_intersection_1, jensen_shannon, kullback_leibler, manhattan, minowski,
+    noelle_1, noelle_2, noelle_3, noelle_4, noelle_5, quadratic_forms,
+    relative_bin_deviation, relative_deviation
+)
+from medpy.metric.image import mutual_information
+from medpy.neighbours.knn import mkneighbors_graph, pdist
+from medpy.utilities import argparseu
+
+mcp = FastMCP("medpy_service")
+
+@mcp.tool(name="log_exception", description="Log an exception using the MedPy logger.")
+def log_exception(exception: Exception) -> dict:
+    """
+    Logs an exception using the MedPy logger.
+
+    Parameters:
+    - exception: Exception instance to be logged.
+
+    Returns:
+    - dict: Contains success status and result message.
+    """
+    try:
+        Logger().exception(exception)
+        return {"success": True, "result": "Exception logged successfully", "error": None}
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+# ===== IO 工具 =====
+@mcp.tool(name="load_image", description="Load medical image from file.")
+def load_image(file_path: str) -> dict:
+    """
+    Loads a medical image from file.
+
+    Parameters:
+    - file_path: Path to the image file.
+
+    Returns:
+    - dict: Contains success status, image data, and metadata.
+    """
+    try:
+        image_data, meta_data = load(file_path)
+        return {
+            "success": True,
+            "result": {
+                "image": image_data.tolist() if isinstance(image_data, np.ndarray) else image_data,
+                "shape": list(image_data.shape) if hasattr(image_data, 'shape') else None,
+                "dtype": str(image_data.dtype) if hasattr(image_data, 'dtype') else None,
+                "metadata": str(meta_data)
+            },
+            "error": None
+        }
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="save_image", description="Save image to file.")
+def save_image(image_data: list, file_path: str, metadata: dict = None) -> dict:
+    """
+    Saves an image to file with optional metadata.
+
+    Parameters:
+    - image_data: Image data as list/array.
+    - file_path: Output file path.
+    - metadata: Optional metadata dictionary.
+
+    Returns:
+    - dict: Contains success status and result message.
+    """
+    try:
+        image_array = np.asarray(image_data)
+        meta = Header() if metadata is None else metadata
+        save(image_array, file_path, meta)
+        return {
+            "success": True,
+            "result": f"Image saved successfully to {file_path}",
+            "error": None
+        }
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+# ===== 二值化和标记处理工具 =====
+@mcp.tool(name="bounding_box", description="Compute bounding box of a binary image.")
+def bounding_box_tool(binary_image: list) -> dict:
+    """
+    Computes the bounding box of a binary image.
+
+    Parameters:
+    - binary_image: Binary image data.
+
+    Returns:
+    - dict: Contains bounding box slices.
+    """
+    try:
+        binary_array = np.asarray(binary_image, dtype=bool)
+        slices = bounding_box(binary_array)
+        return {
+            "success": True,
+            "result": {
+                "slices": str(slices),
+                "bounds": [(s.start, s.stop) for s in slices]
+            },
+            "error": None
+        }
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="largest_connected_component", description="Extract largest connected component from binary image.")
+def largest_connected_component_tool(binary_image: list, connectivity: int = 1) -> dict:
+    """
+    Extracts the largest connected component from a binary image.
+
+    Parameters:
+    - binary_image: Binary image data.
+    - connectivity: Connectivity type (1 or 2, default: 1).
+
+    Returns:
+    - dict: Contains result image with only the largest component.
+    """
+    try:
+        binary_array = np.asarray(binary_image, dtype=bool)
+        result = largest_connected_component(binary_array, connectivity=connectivity)
+        return {
+            "success": True,
+            "result": result.tolist() if isinstance(result, np.ndarray) else result,
+            "error": None
+        }
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="size_threshold", description="Remove small connected components below size threshold.")
+def size_threshold_tool(binary_image: list, size_threshold: int = 100, connectivity: int = 1) -> dict:
+    """
+    Removes connected components smaller than the threshold.
+
+    Parameters:
+    - binary_image: Binary image data.
+    - size_threshold: Minimum size for components to keep (default: 100).
+    - connectivity: Connectivity type (1 or 2, default: 1).
+
+    Returns:
+    - dict: Contains filtered image.
+    """
+    try:
+        binary_array = np.asarray(binary_image, dtype=bool)
+        result = size_threshold(binary_array, size_threshold=size_threshold, connectivity=connectivity)
+        return {
+            "success": True,
+            "result": result.tolist() if isinstance(result, np.ndarray) else result,
+            "error": None
+        }
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="relabel", description="Relabel connected components in a labeled image.")
+def relabel_tool(labeled_image: list, with_background: bool = True) -> dict:
+    """
+    Relabels connected components with consecutive integers.
+
+    Parameters:
+    - labeled_image: Labeled image data.
+    - with_background: Whether to relabel background (default: True).
+
+    Returns:
+    - dict: Contains relabeled image and number of labels.
+    """
+    try:
+        labeled_array = np.asarray(labeled_image)
+        result, num_labels = relabel(labeled_array, with_background=with_background)
+        return {
+            "success": True,
+            "result": {
+                "image": result.tolist() if isinstance(result, np.ndarray) else result,
+                "num_labels": int(num_labels)
+            },
+            "error": None
+        }
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+# ===== 度量工具 =====
+@mcp.tool(name="dice_coefficient", description="Compute Dice coefficient between two binary images.")
+def dice_coefficient_tool(result_image: list, reference_image: list) -> dict:
+    """
+    Computes the Dice coefficient (F1 score) between two binary images.
+
+    Parameters:
+    - result_image: Predicted/result binary image.
+    - reference_image: Ground truth reference image.
+
+    Returns:
+    - dict: Contains Dice coefficient value (0-1, higher is better).
+    """
+    try:
+        result_array = np.asarray(result_image, dtype=bool)
+        reference_array = np.asarray(reference_image, dtype=bool)
+        dice = dc(result_array, reference_array)
+        return {
+            "success": True,
+            "result": {"dice_coefficient": float(dice)},
+            "error": None
+        }
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="hausdorff_distance", description="Compute Hausdorff distance between two binary images.")
+def hausdorff_distance_tool(result_image: list, reference_image: list) -> dict:
+    """
+    Computes the Hausdorff distance between two binary images (distance measure).
+
+    Parameters:
+    - result_image: Predicted/result binary image.
+    - reference_image: Ground truth reference image.
+
+    Returns:
+    - dict: Contains Hausdorff distance and 95% Hausdorff distance.
+    """
+    try:
+        result_array = np.asarray(result_image, dtype=bool)
+        reference_array = np.asarray(reference_image, dtype=bool)
+        hd_dist = hd(result_array, reference_array)
+        hd95_dist = hd95(result_array, reference_array)
+        return {
+            "success": True,
+            "result": {
+                "hausdorff_distance": float(hd_dist),
+                "hausdorff_95": float(hd95_dist)
+            },
+            "error": None
+        }
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="average_surface_distance", description="Compute average surface distance between two binary images.")
+def average_surface_distance_tool(result_image: list, reference_image: list) -> dict:
+    """
+    Computes the average surface distance and symmetric average surface distance.
+
+    Parameters:
+    - result_image: Predicted/result binary image.
+    - reference_image: Ground truth reference image.
+
+    Returns:
+    - dict: Contains ASD and ASSD values.
+    """
+    try:
+        result_array = np.asarray(result_image, dtype=bool)
+        reference_array = np.asarray(reference_image, dtype=bool)
+        asd_dist = asd(result_array, reference_array)
+        assd_dist = assd(result_array, reference_array)
+        return {
+            "success": True,
+            "result": {
+                "average_surface_distance": float(asd_dist),
+                "symmetric_average_surface_distance": float(assd_dist)
+            },
+            "error": None
+        }
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="sensitivity_specificity", description="Compute sensitivity and specificity metrics.")
+def sensitivity_specificity_tool(result_image: list, reference_image: list) -> dict:
+    """
+    Computes sensitivity (recall) and specificity metrics.
+
+    Parameters:
+    - result_image: Predicted/result binary image.
+    - reference_image: Ground truth reference image.
+
+    Returns:
+    - dict: Contains sensitivity, specificity, and related metrics.
+    """
+    try:
+        result_array = np.asarray(result_image, dtype=bool)
+        reference_array = np.asarray(reference_image, dtype=bool)
+        sens = sensitivity(result_array, reference_array)
+        spec = specificity(result_array, reference_array)
+        prec = precision(result_array, reference_array)
+        recall_val = recall(result_array, reference_array)
+        ppv = positive_predictive_value(result_array, reference_array)
+        
+        return {
+            "success": True,
+            "result": {
+                "sensitivity": float(sens),
+                "specificity": float(spec),
+                "precision": float(prec),
+                "recall": float(recall_val),
+                "positive_predictive_value": float(ppv)
+            },
+            "error": None
+        }
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+# ===== 图像滤波工具 =====
+@mcp.tool(name="otsu_threshold", description="Apply Otsu's method for automatic thresholding.")
+def otsu_threshold_tool(image_data: list) -> dict:
+    """
+    Applies Otsu's method to find optimal threshold value.
+
+    Parameters:
+    - image_data: Input image data.
+
+    Returns:
+    - dict: Contains threshold value and binary result.
+    """
+    try:
+        image_array = np.asarray(image_data)
+        threshold = otsu(image_array)
+        binary_result = (image_array >= threshold).astype(int)
+        return {
+            "success": True,
+            "result": {
+                "threshold": float(threshold),
+                "binary_image": binary_result.tolist(),
+                "shape": list(binary_result.shape)
+            },
+            "error": None
+        }
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="gaussian_gradient_magnitude", description="Compute Gaussian gradient magnitude of image.")
+def gaussian_gradient_magnitude_tool(image_data: list, sigma: float = 1.0) -> dict:
+    """
+    Computes the Gaussian gradient magnitude of an image.
+
+    Parameters:
+    - image_data: Input image data.
+    - sigma: Standard deviation for Gaussian filter (default: 1.0).
+
+    Returns:
+    - dict: Contains gradient magnitude image.
+    """
+    try:
+        image_array = np.asarray(image_data, dtype=np.float32)
+        result = gaussian_gradient_magnitude(image_array, sigma=sigma)
+        return {
+            "success": True,
+            "result": result.tolist() if isinstance(result, np.ndarray) else result,
+            "error": None
+        }
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+# ===== 特征提取工具 =====
+@mcp.tool(name="local_histogram", description="Compute local histogram around each voxel.")
+def local_histogram_tool(image_data: list, size: int = 3, bins: int = 32) -> dict:
+    """
+    Computes local histogram around each voxel.
+
+    Parameters:
+    - image_data: Input image data.
+    - size: Size of local region (default: 3).
+    - bins: Number of histogram bins (default: 32).
+
+    Returns:
+    - dict: Contains local histogram features.
+    """
+    try:
+        image_array = np.asarray(image_data, dtype=np.float32)
+        result = local_histogram(image_array, size=size, bins=bins)
+        return {
+            "success": True,
+            "result": {
+                "shape": list(result.shape) if hasattr(result, 'shape') else None,
+                "dtype": str(result.dtype) if hasattr(result, 'dtype') else None,
+                "summary": "Local histogram computed successfully"
+            },
+            "error": None
+        }
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="median_intensity", description="Compute median intensity in image.")
+def median_intensity_tool(image_data: list) -> dict:
+    """
+    Computes the median intensity value.
+
+    Parameters:
+    - image_data: Input image data.
+
+    Returns:
+    - dict: Contains median intensity value.
+    """
+    try:
+        image_array = np.asarray(image_data)
+        result = median(image_array)
+        return {
+            "success": True,
+            "result": {"median_intensity": float(result)},
+            "error": None
+        }
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="compute_fuzzy_histogram", description="Compute a fuzzy histogram from image data.")
+def compute_fuzzy_histogram(image_data: list, bins: int = 10, membership_function: str = "triangular", 
+                           smoothness: float = None, normed: bool = False) -> dict:
+    """
+    Computes a fuzzy histogram from image data with complete parameter control.
+
+    Parameters:
+    - image_data: List of image data.
+    - bins: Number of equal-width bins (default: 10).
+    - membership_function: Type of membership function ('triangular', 'trapezoid', 'gaussian', 'sigmoid').
+    - smoothness: The smoothness parameter for the histogram.
+    - normed: If True, normalize the result as probability density function.
+
+    Returns:
+    - dict: Contains success status, histogram values, and bin edges.
+    """
+    try:
+        image_array = np.asarray(image_data)
+        hist, bin_edges = fuzzy_histogram(image_array, bins=bins, membership=membership_function, 
+                                         smoothness=smoothness, normed=normed)
+        return {
+            "success": True,
+            "result": {
+                "histogram": hist.tolist() if isinstance(hist, np.ndarray) else hist,
+                "bin_edges": bin_edges.tolist() if isinstance(bin_edges, np.ndarray) else bin_edges,
+                "bins": bins,
+                "membership_function": membership_function
+            },
+            "error": None
+        }
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="normalize_features", description="Normalize feature vectors.")
+def normalize_features(features: list) -> dict:
+    """
+    Normalizes feature vectors using L2 normalization.
+
+    Parameters:
+    - features: List of feature vectors.
+
+    Returns:
+    - dict: Contains success status and normalized features.
+    """
+    try:
+        features_array = np.asarray(features)
+        result = normalize(features_array)
+        return {
+            "success": True,
+            "result": result.tolist() if isinstance(result, np.ndarray) else result,
+            "error": None
+        }
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="apply_anisotropic_diffusion", description="Apply anisotropic diffusion to an image.")
+def apply_anisotropic_diffusion(image_data: list, niter: int = 1, kappa: float = 50, 
+                               gamma: float = 0.1, voxelspacing: list = None, option: int = 1) -> dict:
+    """
+    Applies anisotropic diffusion to an image for edge-preserving smoothing.
+
+    Parameters:
+    - image_data: List of image data.
+    - niter: Number of iterations (default: 1).
+    - kappa: Conduction coefficient, 20-100 recommended (default: 50).
+    - gamma: Max value should be ≤ 0.25 for stability (default: 0.1).
+    - voxelspacing: Voxel spacing (optional).
+    - option: Diffusion equation option (default: 1).
+
+    Returns:
+    - dict: Contains success status and smoothed image data.
+    """
+    try:
+        if gamma > 0.25:
+            return {"success": False, "result": None, "error": "gamma must be ≤ 0.25 for stability"}
+        if niter <= 0:
+            return {"success": False, "result": None, "error": "niter must be positive"}
+        
+        image_array = np.asarray(image_data, dtype=np.float32)
+        voxelspacing_array = np.asarray(voxelspacing) if voxelspacing else None
+        
+        result = anisotropic_diffusion(image_array, niter=niter, kappa=kappa, 
+                                      gamma=gamma, voxelspacing=voxelspacing_array, option=option)
+        return {
+            "success": True,
+            "result": result.tolist() if isinstance(result, np.ndarray) else result,
+            "error": None
+        }
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+@mcp.tool(name="segment_with_graphcut", description="Perform graphcut segmentation on an image.")
+def segment_with_graphcut(image_data: list, fg_markers: list, bg_markers: list, 
+                         boundary_term_type: str = "difference_of_means", 
+                         boundary_term_args: list = None) -> dict:
+    """
+    Performs graphcut segmentation on an image with specified boundary term.
+
+    Parameters:
+    - image_data: List of image data.
+    - fg_markers: Foreground markers.
+    - bg_markers: Background markers.
+    - boundary_term_type: Type of boundary term ('difference_of_means', 'stawiaski', 'division', etc).
+    - boundary_term_args: Arguments for the boundary term function.
+
+    Returns:
+    - dict: Contains success status and segmented image data.
+    """
+    try:
+        image_array = np.asarray(image_data)
+        fg_markers_array = np.asarray(fg_markers, dtype=bool)
+        bg_markers_array = np.asarray(bg_markers, dtype=bool)
+        boundary_term_args = tuple(boundary_term_args) if boundary_term_args else ()
+        
+        # Map boundary term names to functions
+        boundary_functions = {
+            "difference_of_means": boundary_difference_of_means,
+            "stawiaski": boundary_stawiaski,
+            "stawiaski_directed": boundary_stawiaski_directed,
+            "division": boundary_difference_division,
+            "exponential": boundary_difference_exponential,
+            "linear": boundary_difference_linear,
+            "power": boundary_difference_power,
+            "maximum_division": boundary_maximum_division,
+            "maximum_exponential": boundary_maximum_exponential,
+            "maximum_linear": boundary_maximum_linear,
+            "maximum_power": boundary_maximum_power,
+        }
+        
+        if boundary_term_type not in boundary_functions:
+            return {"success": False, "result": None, 
+                   "error": f"Unknown boundary_term_type: {boundary_term_type}"}
+        
+        boundary_func = boundary_functions[boundary_term_type]
+        gcgraph = graph_from_voxels(fg_markers_array, bg_markers_array, boundary_func, boundary_term_args)
+        maxflow = gcgraph.maxflow()
+        result = gcgraph.get_segmented_image()
+        
+        return {
+            "success": True,
+            "result": result.tolist() if isinstance(result, np.ndarray) else result,
+            "maxflow": float(maxflow),
+            "error": None
+        }
+    except Exception as e:
+        return {"success": False, "result": None, "error": str(e)}
+
+def create_app() -> FastMCP:
+    """
+    Creates and returns the FastMCP application instance.
+
+    Returns:
+    - FastMCP: The FastMCP application instance.
+    """
+    return mcp

medpy/mcp_output/requirements.txt

@@ -0,0 +1,8 @@
+fastmcp>=0.1.0
+pydantic>=2.0.0
+numpy
+scipy
+matplotlib
+
+# Optional Dependencies
+# pydicom

medpy/mcp_output/simple_revise_error_analysis.json

@@ -0,0 +1,6 @@
+{
+  "status": "FAIL",
+  "next_action": "fix_directly",
+  "confidence": 0.9,
+  "summary": "The error is due to a missing 'numpy' module, which is required by the script. This can be fixed directly by ensuring that the 'numpy' package is installed in the conda environment being used. The error message indicates that the script is being run using 'conda run', so the appropriate fix is to activate the conda environment and install 'numpy' using the command 'conda install numpy'."
+}

medpy/mcp_output/start_mcp.py

@@ -0,0 +1,33 @@
+"""
+MCP Service Startup Entry
+"""
+import sys
+import os
+
+project_root = os.path.dirname(os.path.abspath(__file__))
+mcp_plugin_dir = os.path.join(project_root, "mcp_plugin")
+if mcp_plugin_dir not in sys.path:
+    sys.path.insert(0, mcp_plugin_dir)
+
+# Set path to source directory
+source_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), "source")
+sys.path.insert(0, source_path)
+
+from mcp_service import create_app
+
+def main():
+    """Start FastMCP service"""
+    app = create_app()
+    # Use environment variable to configure port, default 8000
+    port = int(os.environ.get("MCP_PORT", "8000"))
+    
+    # Choose transport mode based on environment variable
+    transport = os.environ.get("MCP_TRANSPORT", "stdio")
+    if transport == "http":
+        app.run(transport="http", host="0.0.0.0", port=port)
+    else:
+        # Default to STDIO mode
+        app.run()
+
+if __name__ == "__main__":
+    main()

medpy/mcp_output/tests_mcp/test_mcp_basic.py

@@ -0,0 +1,49 @@
+"""
+MCP Service Basic Test
+"""
+import sys
+import os
+
+project_root = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
+mcp_plugin_dir = os.path.join(project_root, "mcp_plugin")
+if mcp_plugin_dir not in sys.path:
+    sys.path.insert(0, mcp_plugin_dir)
+
+source_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__)))), "source")
+sys.path.insert(0, source_path)
+
+def test_import_mcp_service():
+    """Test if MCP service can be imported normally"""
+    try:
+        from mcp_service import create_app
+        app = create_app()
+        assert app is not None
+        print("MCP service imported successfully")
+        return True
+    except Exception as e:
+        print("MCP service import failed: " + str(e))
+        return False
+
+def test_adapter_init():
+    """Test if adapter can be initialized normally"""
+    try:
+        from adapter import Adapter
+        adapter = Adapter()
+        assert adapter is not None
+        print("Adapter initialized successfully")
+        return True
+    except Exception as e:
+        print("Adapter initialization failed: " + str(e))
+        return False
+
+if __name__ == "__main__":
+    print("Running MCP service basic test...")
+    test1 = test_import_mcp_service()
+    test2 = test_adapter_init()
+    
+    if test1 and test2:
+        print("All basic tests passed")
+        sys.exit(0)
+    else:
+        print("Some tests failed")
+        sys.exit(1)

medpy/mcp_output/tests_smoke/test_smoke.py

@@ -0,0 +1,29 @@
+import importlib, sys
+import os
+
+# Add current directory to Python path
+sys.path.insert(0, os.getcwd())
+
+source_dir = os.path.join(os.getcwd(), "source")
+if os.path.exists(source_dir):
+    sys.path.insert(0, source_dir)
+
+
+try:
+    importlib.import_module("medpy")
+    print("OK - Successfully imported medpy")
+except ImportError as e:
+    print(f"Failed to import medpy: {e}")
+    fallback_packages = []
+
+    fallback_packages = ['medpy']
+
+    for pkg in fallback_packages:
+        try:
+            importlib.import_module(pkg)
+            print(f"OK - Successfully imported {pkg}")
+            break
+        except ImportError:
+            continue
+    else:
+        print("All import attempts failed")

medpy/source/.github/workflows/README.md

@@ -0,0 +1,16 @@
+# MedPy's CI/CD workflows
+
+## Build & release
+Upon creating a release or a pre-release on GitHub, the package is *build* and *published* to [test.pypi.org](https://test.pypi.org).
+
+Install from test PyPi with `python -m pip install --index-url https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple medpy==x.y.z.`. This ensures that the dependencies are installed from the proper PyPI.
+
+After making sure that the package published there is installable and passes all tests, the final *publish* to [pypi.org](https://pypi.org) can be triggered manually from the GitHub UI.
+
+Note that publishing only works for releases created directly from the `master` branch. Releasees published from other branches should always be pre-releases and never published to [pypi.org](https://pypi.org), but only [test.pypi.org](https://test.pypi.org).
+
+## pre-commit.yml
+Makes sure that all PRs and all releases adhere to the pre-commit rules.
+
+## run-test*.yml
+Makes sure that all PRs and all releases pass the tests.

medpy/source/.github/workflows/build-publish-test.yml

@@ -0,0 +1,54 @@
+# Build package & publish a release to PyPI (test)
+# Given a tag, downloads the associated code, builds the package, and uploads the source tarball as artifact
+# This version releases to https://test.pypi.org/ for testing purposes
+# Triggers on: all published releases (incl pre-releases)
+
+name: Build package & release to PyPI (test)
+
+on:
+  release:
+    types: [published]
+
+permissions:
+  contents: read
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: 3.x
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install build
+      - name: Build a source tarball
+        run: python -m build --sdist
+      - name: Store the distribution packages
+        uses: actions/upload-artifact@v4.3.0
+        with:
+          name: python-package-distributions-${{ github.ref_name }}
+          path: dist/
+
+  publish-test:
+    needs:
+      - build
+    runs-on: ubuntu-latest
+    environment:
+      name: pypi-publish-test
+      url: https://test.pypi.org/p/medpy
+    permissions:
+      id-token: write # IMPORTANT: mandatory for trusted publishing
+    steps:
+      - name: Download dists
+        uses: actions/download-artifact@v4.1.1 # make sure that same major version as actions/upload-artifact
+        with:
+          name: python-package-distributions-${{ github.ref_name }}
+          path: dist/
+      - name: Publish package
+        uses: pypa/gh-action-pypi-publish@v1.8.11
+        with:
+          repository-url: https://test.pypi.org/legacy/ # test publish platform

medpy/source/.github/workflows/pre-commit.yml

@@ -0,0 +1,30 @@
+# Runs the pre-commit hooks to make sure that all changes are properly formatted and such
+# Triggers on: All PRs that are mergable, but not for draft PRs
+# Triggers on: all published releases (incl draft releases)
+
+name: Pre-commit hooks
+
+on:
+  pull_request:
+    types: [opened, synchronize, reopened, ready_for_review]
+  release:
+    types: [published]
+
+permissions:
+  contents: read
+
+jobs:
+  pre-commit:
+    if: github.event.pull_request.draft == false
+
+    runs-on: ubuntu-latest
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: 3.x
+      - uses: pre-commit/action@v3.0.0
+      - uses: pre-commit-ci/lite-action@v1.0.1
+        if: always()

medpy/source/.github/workflows/publish.yml

@@ -0,0 +1,56 @@
+# Publish a release to PyPI
+# Requires build package workflow to run first
+# This version releases to https://pypi.org/, only trigger if the release has been thorough tested
+
+name: Build package & release to PyPI
+
+on:
+  workflow_dispatch:
+    inputs:
+      tag:
+        description: "Select release to publish"
+        required: true
+
+permissions:
+  contents: read
+
+jobs:
+  build:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+        with:
+          ref: ${{ inputs.tag }}
+      - name: Set up Python
+        uses: actions/setup-python@v5
+        with:
+          python-version: 3.x
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install build
+      - name: Build a source tarball
+        run: python -m build --sdist
+      - name: Store the distribution packages
+        uses: actions/upload-artifact@v4.3.0
+        with:
+          name: python-package-distributions-${{ inputs.tag }}
+          path: dist/
+
+  publish:
+    needs:
+      - build
+    runs-on: ubuntu-latest
+    environment:
+      name: pypi-publish
+      url: https://pypi.org/p/medpy
+    permissions:
+      id-token: write # IMPORTANT: mandatory for trusted publishing
+    steps:
+      - name: Download dists
+        uses: actions/download-artifact@v4.1.1 # make sure that same major version as actions/upload-artifact
+        with:
+          name: python-package-distributions-${{ inputs.tag }}
+          path: dist/
+      - name: Publish package
+        uses: pypa/gh-action-pypi-publish@v1.8.11

medpy/source/.github/workflows/run-tests-gc.yml

@@ -0,0 +1,55 @@
+# Install the package and run the graph-cut tests
+# This test is kept separate, as the graphcut functionality is optional and unstable
+# Triggers on: All PRs that are mergable, but not for draft PRs
+# Triggers on: all published releases (incl pre-releases)
+
+# Note: the dependency libboost_python will always be installed against the OS's main python version,
+# independent of the python version set-up. They are 22.04 = 3.10 and 20.04 = 3.8.
+
+name: Run tests (graphcut only)
+
+on:
+  pull_request:
+    types: [opened, synchronize, reopened, ready_for_review]
+  release:
+    types: [published]
+
+permissions:
+  contents: read
+
+jobs:
+  run-tests-gc-ubuntu-22_04:
+    if: github.event.pull_request.draft == false
+    runs-on: ubuntu-22.04
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python 3.10
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.10"
+      - name: Install system dependencies for graphcut functionality
+        run: sudo apt-get install -y libboost-python-dev build-essential
+      - name: Install with test dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install -v .[test]
+      - name: Test with pytest (graphcut test only)
+        run: cd tests && pytest graphcut_/*
+
+  run-tests-gc-test-ubuntu-20_04:
+    if: github.event.pull_request.draft == false
+    runs-on: ubuntu-20.04
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python 3.8
+        uses: actions/setup-python@v5
+        with:
+          python-version: "3.8"
+      - name: Install system dependencies for graphcut functionality
+        run: sudo apt-get install -y libboost-python-dev build-essential
+      - name: Install with test dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install -v .[test]
+      - name: Test with pytest (graphcut test only)
+        run: cd tests && pytest graphcut_/*

medpy/source/.github/workflows/run-tests.yml

@@ -0,0 +1,43 @@
+# Install the package and run all tests except the graph-cut ones
+# Triggers on: All PRs that are mergable, but not for draft PRs
+# Triggers on: all published releases (incl pre-releases)
+
+name: Run tests (wo graphcut)
+
+on:
+  pull_request:
+    types: [opened, synchronize, reopened, ready_for_review]
+  release:
+    types: [published]
+
+permissions:
+  contents: read
+
+jobs:
+  run-tests:
+    if: github.event.pull_request.draft == false
+
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        os: [ubuntu-latest, macos-latest]
+
+    runs-on: ${{ matrix.os }}
+
+    steps:
+      - uses: actions/checkout@v4
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Install with test dependencies
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install .[test]
+      - name: Test with pytest
+        run: |
+          pytest tests/features_/*
+          pytest tests/filter_/*
+          pytest tests/io_/*
+          pytest tests/metric_/*

medpy/source/.gitignore

@@ -0,0 +1,97 @@
+TODO.txt
+
+
+# Images
+*.nii
+*.mhd
+*.raw
+
+# Local virtual envs
+.venv/
+
+# DOC dirs
+doc/build/
+doc/generated/
+doc/source/generated/
+
+# Notebooks dirs
+.ipynb_checkpoints
+
+# BUILD dirs
+build/
+dist/
+MedPy.egg-info/
+
+# Only locally used, temporary .py scripts.
+_*.py
+!__init__.py
+
+# Backup files
+*.bak
+
+# Compiled source
+*.com
+*.class
+*.dll
+*.exe
+*.o
+*.so
+*.pyc
+*.pyo
+
+# Packages
+# it's better to unpack these files and commit the raw source
+# git has its own built in compression methods
+*.7z
+*.dmg
+*.gz
+*.iso
+*.jar
+*.rar
+*.tar
+*.zip
+
+# Logs and databases
+*.log
+*.sql
+*.sqlite
+
+# OS generated files
+.DS_Store*
+ehthumbs.db
+Icon?
+Thumbs.db
+*~
+
+# Eclipse and PyDev project files
+.project
+.pydevproject
+.settings/
+.metadata/
+
+# Suggestions by GitHub for Python projects
+# Packages
+*.egg
+*.egg-info
+dist
+build
+eggs
+parts
+var
+sdist
+develop-eggs
+.installed.cfg
+
+# Installer logs
+pip-log.txt
+
+# Unit test / coverage reports
+.coverage
+.tox
+.hypothesis
+
+#Translations
+*.mo
+
+#Mr Developer
+.mr.developer.cfg

medpy/source/.pre-commit-config.yaml

@@ -0,0 +1,35 @@
+default_stages: [commit]
+repos:
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+      - id: check-added-large-files
+      - id: check-merge-conflict
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: trailing-whitespace
+      - id: debug-statements
+
+  - repo: https://github.com/pycqa/isort
+    rev: "5.13.2"
+    hooks:
+      - id: isort
+        args: ["--profile", "black", "--line-length=88"]
+
+  - repo: https://github.com/psf/black
+    rev: 23.12.0
+    hooks:
+      - id: black
+        args: ["--line-length=88"]
+
+  - repo: https://github.com/hadialqattan/pycln
+    rev: "v2.4.0"
+    hooks:
+      - id: pycln
+        args: ["--all"]
+
+  - repo: https://github.com/Yelp/detect-secrets
+    rev: v1.4.0
+    hooks:
+      - id: detect-secrets
+        args: ["--exclude-files", ".*\\.ipynb"]

medpy/source/CHANGES.txt

@@ -0,0 +1,17 @@
+v0.5.2, 2024-07-23 -- Hotfixes
+v0.5.1, 2024-04-03 -- Hotfixes
+v0.5.0, 2024-04-03 -- Addressed all depreciation warnings and incompatabilities
+                      Updated documentation
+                      Updated and fixed tests
+                      Added github workflows as system
+                      Introduced formatting rules and pre-commit to enforce them
+                      Removed dockerfile creation files
+v0.4.0, 2018-02-XX -- Switched to Python 3: finally compatible with modern development environements
+                      Switched to simple itk for image loading/saving: read dicom series, more formats, less dependencies, cleaner code, easier to maintain
+                      Documentation: installation instructions for Windows and OsX
+                      Others: improved filters, cleanup, bugfixes
+v0.3.0, 2017-09-20 -- Extensive cleanup, many new functionalities, updated documentation, notebook tutorials, Python 3 branch
+v0.2.2, 2014-09-18 -- Changes the documentation engine to Sphinx and fixed a number of bugs
+v0.2.1, 2014-08-19 -- ez_setup.py has not been include
+v0.2.0, 2014-08-19 -- Little clean-up, many new functionalities; in generally simpler structure and usage; complilation of C++ module not required anymore
+v0.1.0, 2013-04-15 -- Initial release.

medpy/source/LICENSE.txt

@@ -0,0 +1,674 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
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+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
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+
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+  You should also get your employer (if you work as a programmer) or school,
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+
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+<http://www.gnu.org/philosophy/why-not-lgpl.html>.

medpy/source/MANIFEST.in

@@ -0,0 +1,7 @@
+include *.txt
+include *.md
+
+include lib/maxflow/src/*.h
+include lib/maxflow/src/*.cpp
+include lib/maxflow/src/instances.inc
+include lib/maxflow/src/README

medpy/source/README.md

@@ -0,0 +1,38 @@
+[![PyPI version](https://badge.fury.io/py/MedPy.svg)](https://pypi.python.org/pypi/MedPy/)
+[![anaconda version](https://anaconda.org/conda-forge/medpy/badges/version.svg)](https://anaconda.org/conda-forge/medpy)
+[![PyPI pyversions](https://img.shields.io/pypi/pyversions/MedPy.svg)](https://pypi.python.org/pypi/MedPy/)
+[![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)
+[![Downloads](https://pepy.tech/badge/medpy/month)](https://pepy.tech/project/medpy)
+[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.2565940.svg)](https://doi.org/10.5281/zenodo.2565940)
+
+
+[GitHub](https://github.com/loli/medpy/) | [Documentation](http://loli.github.io/medpy/) | [Tutorials](http://loli.github.io/medpy/) | [Issue tracker](https://github.com/loli/medpy/issues)
+
+# medpy - Medical Image Processing in Python
+
+MedPy is an image processing library and collection of scripts targeted towards medical (i.e. high dimensional) image processing.
+
+## Stable releases
+
+- Download (stable release): https://pypi.python.org/pypi/medpy
+- HTML documentation and installation instruction (stable release): http://loli.github.io/medpy/
+- Download from [Conda-Forge](https://conda-forge.org): https://anaconda.org/conda-forge/medpy
+
+## Development version
+
+- Download (development version): https://github.com/loli/medpy
+- HTML documentation and installation instruction (development version): create this from doc/ folder following instructions in contained README file
+
+## Contribute
+
+- Clone `master` branch from [github](https://github.com/loli/medpy)
+- Install [pre-commit](https://pre-commit.com/) hooks or with `[dev,test]` extras
+- Submit your change as a PR request
+
+## Python 2 version
+
+Python 2 is no longer supported. But you can still use the older releases `<=0.3.0`.
+
+## Other links
+
+- Issue tracker: https://github.com/loli/medpy/issues

medpy/source/README_PYPI.md

@@ -0,0 +1,157 @@
+# MedPy
+
+[GitHub](https://github.com/loli/medpy/) | [Documentation](http://loli.github.io/medpy/) | [Tutorials](http://loli.github.io/medpy/) | [Issue tracker](https://github.com/loli/medpy/issues)
+
+**MedPy** is a library and script collection for medical image processing in Python, providing basic functionalities for **reading**, **writing** and **manipulating** large images of **arbitrary dimensionality**.
+Its main contributions are n-dimensional versions of popular **image filters**, a collection of **image feature extractors**, ready to be used with [scikit-learn](http://scikit-learn.org), and an exhaustive n-dimensional **graph-cut** package.
+
+* [Installation](#installation)
+* [Getting started with the library](#getting-started-with-the-library)
+* [Getting started with the scripts](#getting-started-with-the-scripts)
+* [Support of medical image formats](#support-of-medical-image-formats)
+* [Requirements](#requirements)
+* [License](#license)
+
+## Installation
+
+```bash
+sudo apt-get install libboost-python-dev build-essential
+pip3 install medpy
+```
+
+**MedPy** requires **Python 3** and officially supports Ubuntu as well as other Debian derivatives.
+For installation instructions on other operating systems see the [documentation](http://loli.github.io/medpy/).
+While the library itself is written purely in Python, the **graph-cut** extension comes in C++ and has [it's own requirements](http://loli.github.io/medpy/installation/graphcutsupport.html).
+
+## Getting started with the library
+
+If you already have a medical image at hand in [one of the supported formats](http://loli.github.io/medpy/information/imageformats.html), you can use it for this introduction. If not, navigate to http://www.nitrc.org/projects/inia19, click on the *Download Now* button, unpack and look for the *inia19-t1.nii* file. Open it in your favorite medical image viewer (I personally fancy [itksnap](http://www.itksnap.org)) and beware: the INIA19 primate brain atlas.
+
+Load the image
+
+```python
+from medpy.io import load
+image_data, image_header = load('/path/to/image.xxx')
+```
+
+The data is stored in a numpy ndarray, the header is an object containing additional metadata, such as the voxel-spacing. Now lets take a look at some of the image metadata
+
+```python
+image_data.shape
+```
+
+`(168, 206, 128)`
+
+```python
+image_data.dtype
+```
+
+`dtype(float32)`
+
+And the header gives us
+
+```python
+image_header.get_voxel_spacing()
+```
+
+`(0.5, 0.5, 0.5)`
+
+```python
+image_header.get_offset()
+```
+
+`(0.0, 0.0, 0.0)`
+
+Now lets apply one of the **MedPy** filter, more exactly the [Otsu thresholding](https://en.wikipedia.org/wiki/Otsu%27s_method), which can be used for automatic background removal
+
+```python
+from medpy.filter import otsu
+threshold = otsu(image_data)
+output_data = image_data > threshold
+```
+
+And save the binary image, marking the foreground
+
+```python
+from medpy.io import save
+save(output_data, '/path/to/otsu.xxx', image_header)
+```
+
+After taking a look at it, you might want to dive deeper with the tutorials found in the [documentation](http://loli.github.io/medpy/information/commandline_tools_listing.html).
+
+## Getting started with the scripts
+
+**MedPy** comes with a range of read-to-use commandline scripts, which are all prefixed by `medpy_`.
+To try these examples, first get an image as described in the previous section. Now call
+
+```bash
+medpy_info.py /path/to/image.xxx
+```
+
+will give you some details about the image. With
+
+```bash
+medpy_diff.py /path/to/image1.xxx /path/to/image2.xxx
+```
+
+you can compare two image. And
+
+```bash
+medpy_anisotropic_diffusion.py /path/to/image.xxx /path/to/output.xxx
+```
+
+lets you apply an edge preserving anisotropic diffusion filter. For a list of all scripts, see the [documentation](http://loli.github.io/medpy/).
+
+## Support of medical image formats
+
+MedPy relies on SimpleITK, which enables the power of ITK for image loading and saving.
+The supported image file formats should include at least the following. Note that not all might be supported by your machine.
+
+**Medical formats:**
+
+* ITK MetaImage (.mha/.raw, .mhd)
+* Neuroimaging Informatics Technology Initiative (NIfTI) (.nia, .nii, .nii.gz, .hdr, .img, .img.gz)
+* Analyze (plain, SPM99, SPM2) (.hdr/.img, .img.gz)
+* Digital Imaging and Communications in Medicine (DICOM) (.dcm, .dicom)
+* Digital Imaging and Communications in Medicine (DICOM) series (<directory>/)
+* Nearly Raw Raster Data (Nrrd) (.nrrd, .nhdr)
+* Medical Imaging NetCDF (MINC) (.mnc, .MNC)
+* Guys Image Processing Lab (GIPL) (.gipl, .gipl.gz)
+
+**Microscopy formats:**
+
+* Medical Research Council (MRC) (.mrc, .rec)
+* Bio-Rad (.pic, .PIC)
+* LSM (Zeiss) microscopy images (.tif, .TIF, .tiff, .TIFF, .lsm, .LSM)
+* Stimulate / Signal Data (SDT) (.sdt)
+
+**Visualization formats:**
+
+* VTK images (.vtk)
+
+**Other formats:**
+
+* Portable Network Graphics (PNG) (.png, .PNG)
+* Joint Photographic Experts Group (JPEG) (.jpg, .JPG, .jpeg, .JPEG)
+* Tagged Image File Format (TIFF) (.tif, .TIF, .tiff, .TIFF)
+* Windows bitmap (.bmp, .BMP)
+* Hierarchical Data Format (HDF5) (.h5 , .hdf5 , .he5)
+* MSX-DOS Screen-x (.ge4, .ge5)
+
+## Requirements
+
+MedPy comes with a number of dependencies and optional functionality that can require you to install additional packages.
+
+### Main dependencies
+
+* [scipy](http://www.scipy.org)
+* [numpy](http://www.numpy.org)
+* [SimpleITK](https://simpleitk.readthedocs.io)
+
+### Optional functionalities
+
+* compilation with `max-flow/min-cut` (enables the GraphCut functionalities)
+
+## License
+
+MedPy is distributed under the GNU General Public License, a version of which can be found in the LICENSE.txt file.

medpy/source/RELEASE.md

@@ -0,0 +1,28 @@
+# Steps for a new release
+
+## Preparations
+- Create a branch `Release_x.y.z` to work towards the release
+- Bump up the library version
+    - `setup.py`
+    - `medpy/__init__.py`
+    - `doc/source/conf.py`
+- Run tests and make sure that all work
+- Run notebooks and make sure that all work
+- Check documentation and make sure that up to date
+- Update `CHANGES.txt`, highlighting only major changes
+- Test releases by publishing a pre-release, using the workflow detailed under [.github/workflows](.github/workflows)
+- Re-create documentation and upload to gihub pages to test, then revert to previous version
+
+
+## Release
+- Open PR to master, review, and merge
+- Create a pre-release from master and test
+- Create final release from master and test
+    - You might need to delete test package with same version number ion from test.pypi.org
+- Trigger publish to PyPi workflow (see under [.github/workflows](.github/workflows))
+- Update conda-force recipe to new version (PR)
+- Update DOI
+
+## Further readings
+- https://packaging.python.org/
+- https://docs.github.com/en/actions

medpy/source/__init__.py

@@ -0,0 +1,4 @@
+# -*- coding: utf-8 -*-
+"""
+medpy Project Package Initialization File
+"""

medpy/source/bin/medpy_anisotropic_diffusion.py

@@ -0,0 +1,150 @@
+#!/usr/bin/env python
+
+"""
+Executes gradient anisotropic diffusion filter over an image.
+
+Copyright (C) 2013 Oskar Maier
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+# build-in modules
+import argparse
+import logging
+import os
+
+from medpy.core import Logger
+from medpy.filter.smoothing import anisotropic_diffusion
+
+# own modules
+from medpy.io import get_pixel_spacing, load, save
+
+# third-party modules
+
+# path changes
+
+
+# information
+__author__ = "Oskar Maier"
+__version__ = "r0.1.0, 2013-08-24"
+__email__ = "oskar.maier@googlemail.com"
+__status__ = "Release"
+__description__ = """
+                  Executes gradient anisotropic diffusion filter over an image.
+                  This smoothing algorithm is edges preserving.
+                  To achieve the best effects, the image should be scaled to
+                  values between 0 and 1 beforehand.
+
+                  Note that the images voxel-spacing will be taken into account.
+
+                  Copyright (C) 2013 Oskar Maier
+                  This program comes with ABSOLUTELY NO WARRANTY; This is free software,
+                  and you are welcome to redistribute it under certain conditions; see
+                  the LICENSE file or <http://www.gnu.org/licenses/> for details.
+                  """
+
+
+# code
+def main():
+    # parse cmd arguments
+    parser = getParser()
+    parser.parse_args()
+    args = getArguments(parser)
+
+    # prepare logger
+    logger = Logger.getInstance()
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+    elif args.verbose:
+        logger.setLevel(logging.INFO)
+
+    # check if output image exists (will also be performed before saving, but as the smoothing might be very time intensity, a initial check can save frustration)
+    if not args.force:
+        if os.path.exists(args.output):
+            raise parser.error(
+                "The output image {} already exists.".format(args.output)
+            )
+
+    # loading image
+    data_input, header_input = load(args.input)
+
+    # apply the watershed
+    logger.info(
+        "Applying anisotropic diffusion with settings: niter={} / kappa={} / gamma={}...".format(
+            args.iterations, args.kappa, args.gamma
+        )
+    )
+    data_output = anisotropic_diffusion(
+        data_input,
+        args.iterations,
+        args.kappa,
+        args.gamma,
+        get_pixel_spacing(header_input),
+    )
+
+    # save file
+    save(data_output, args.output, header_input, args.force)
+
+    logger.info("Successfully terminated.")
+
+
+def getArguments(parser):
+    "Provides additional validation of the arguments collected by argparse."
+    return parser.parse_args()
+
+
+def getParser():
+    "Creates and returns the argparse parser object."
+    parser = argparse.ArgumentParser(description=__description__)
+    parser.add_argument("input", help="Source volume.")
+    parser.add_argument("output", help="Target volume.")
+    parser.add_argument(
+        "-i",
+        "--iterations",
+        type=int,
+        default=1,
+        help="The number of smoothing iterations. Strong parameter.",
+    )
+    parser.add_argument(
+        "-k",
+        "--kappa",
+        type=int,
+        default=50,
+        help="The algorithms kappa parameter. The higher the more edges are smoothed over.",
+    )
+    parser.add_argument(
+        "-g",
+        "--gamma",
+        type=float,
+        default=0.1,
+        help="The algorithms gamma parameter. The higher, the stronger the plateaus between edges are smeared.",
+    )
+    parser.add_argument(
+        "-v", dest="verbose", action="store_true", help="Display more information."
+    )
+    parser.add_argument(
+        "-d", dest="debug", action="store_true", help="Display debug information."
+    )
+    parser.add_argument(
+        "-f",
+        dest="force",
+        action="store_true",
+        help="Silently override existing output images.",
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    main()

medpy/source/bin/medpy_apparent_diffusion_coefficient.py

@@ -0,0 +1,218 @@
+#!/usr/bin/env python
+
+"""
+Computes the apparent diffusion coefficient from two diffusion weighted MRI images.
+
+Copyright (C) 2013 Oskar Maier
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+# build-in modules
+import argparse
+import logging
+
+# third-party modules
+import numpy
+from scipy.ndimage import binary_dilation, binary_erosion, binary_fill_holes
+
+# own modules
+from medpy.core import Logger
+from medpy.core.exceptions import ArgumentError
+from medpy.filter import otsu
+from medpy.filter.binary import largest_connected_component
+from medpy.io import header, load, save
+
+# path changes
+
+
+# information
+__author__ = "Oskar Maier"
+__version__ = "r0.1.1, 2013-07-18"
+__email__ = "oskar.maier@googlemail.com"
+__status__ = "Release"
+__description__ = """
+Computes the apparent diffusion coefficient from two diffusion weighted
+MRI images. The output image will be of type float.
+
+Normally diffusion weight (DW) MRI images are acquired once with a
+b-value of 0 (which we call b0) and once with another b-value (which we
+call bx) such as 500, 800 or 1000. The latter is typical for brain MRIs.
+This results in a single b0 DW image and three bx DW images, one for each
+direction.
+
+Usually the three bx DW images are already combined into an isotropic
+average image (which we call abx) denoting the length of the three-dimensional
+vector formed by the the three bx images.
+
+The formula presented in [1] is applied to the b0 and abx images to
+compute the apparent diffusion coefficient (ADC):
+
+ADC = -bx-value * ln(abx-image / b0-image)
+
+To cope with zero-values in the images, we apply a-priori a
+thresholding to the b0 + abx DW image, set all lower values to 0 and
+apply the formula only to the remaining intensities. Note that the
+default threshold is chosen using Otsu's and is good for most cases.
+(Thanks to Nils at the UKE in Hamburg, Germany for this hint!)
+
+We restrain from implementing a method working on more DW images, that
+were acquired with multiple b-values, as [2] observed that this might
+lead to worse results.
+
+[1] "Understanding Diffusion MR Imaging Techniques: From Scalar
+Diffusion-weighted Imaging to Diffusion Tensor Imaging and Beyond" by
+Patric Hagmann et al.
+[2] "Understanding the Mathematics Involved in Calculating Apparent
+Diffusion Coefficient Maps" by Michael Yong Park and Jae Young Byun
+
+Copyright (C) 2013 Oskar Maier
+This program comes with ABSOLUTELY NO WARRANTY; This is free software,
+and you are welcome to redistribute it under certain conditions; see
+the LICENSE file or <http://www.gnu.org/licenses/> for details.
+                  """
+
+
+# code
+def main():
+    args = getArguments(getParser())
+
+    # prepare logger
+    logger = Logger.getInstance()
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+    elif args.verbose:
+        logger.setLevel(logging.INFO)
+
+    # loading input images
+    b0img, b0hdr = load(args.b0image)
+    bximg, bxhdr = load(args.bximage)
+
+    # convert to float
+    b0img = b0img.astype(float)
+    bximg = bximg.astype(float)
+
+    # check if image are compatible
+    if not b0img.shape == bximg.shape:
+        raise ArgumentError(
+            "The input images shapes differ i.e. {} != {}.".format(
+                b0img.shape, bximg.shape
+            )
+        )
+    if not header.get_pixel_spacing(b0hdr) == header.get_pixel_spacing(bxhdr):
+        raise ArgumentError(
+            "The input images voxel spacing differs i.e. {} != {}.".format(
+                header.get_pixel_spacing(b0hdr), header.get_pixel_spacing(bxhdr)
+            )
+        )
+
+    # check if supplied threshold value as well as the b value is above 0
+    if args.threshold is not None and not args.threshold >= 0:
+        raise ArgumentError(
+            "The supplied threshold value must be greater than 0, otherwise a division through 0 might occur."
+        )
+    if not args.b > 0:
+        raise ArgumentError("The supplied b-value must be greater than 0.")
+
+    # compute threshold value if not supplied
+    if args.threshold is None:
+        b0thr = otsu(b0img, 32) / 4.0  # divide by 4 to decrease impact
+        bxthr = otsu(bximg, 32) / 4.0
+        if 0 >= b0thr:
+            raise ArgumentError(
+                "The supplied b0image seems to contain negative values."
+            )
+        if 0 >= bxthr:
+            raise ArgumentError(
+                "The supplied bximage seems to contain negative values."
+            )
+    else:
+        b0thr = bxthr = args.threshold
+
+    logger.debug("thresholds={}/{}, b-value={}".format(b0thr, bxthr, args.b))
+
+    # threshold b0 + bx DW image to obtain a mask
+    # b0 mask avoid division through 0, bx mask avoids a zero in the ln(x) computation
+    mask = binary_fill_holes(b0img > b0thr) & binary_fill_holes(bximg > bxthr)
+
+    # perform a number of binary morphology steps to select the brain only
+    mask = binary_erosion(mask, iterations=1)
+    mask = largest_connected_component(mask)
+    mask = binary_dilation(mask, iterations=1)
+
+    logger.debug(
+        "excluding {} of {} voxels from the computation and setting them to zero".format(
+            numpy.count_nonzero(mask), numpy.prod(mask.shape)
+        )
+    )
+
+    # compute the ADC
+    adc = numpy.zeros(b0img.shape, b0img.dtype)
+    adc[mask] = -1.0 * args.b * numpy.log(bximg[mask] / b0img[mask])
+    adc[adc < 0] = 0
+
+    # saving the resulting image
+    save(adc, args.output, b0hdr, args.force)
+
+
+def getArguments(parser):
+    "Provides additional validation of the arguments collected by argparse."
+    return parser.parse_args()
+
+
+def getParser():
+    "Creates and returns the argparse parser object."
+    parser = argparse.ArgumentParser(
+        description=__description__,
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    parser.add_argument(
+        "b0image", help="the diffusion weighted image required with b=0"
+    )
+    parser.add_argument(
+        "bximage", help="the diffusion weighted image required with b=x"
+    )
+    parser.add_argument(
+        "b", type=int, help="the b-value used to acquire the bx-image (i.e. x)"
+    )
+    parser.add_argument(
+        "output", help="the computed apparent diffusion coefficient image"
+    )
+
+    parser.add_argument(
+        "-t",
+        "--threshold",
+        type=int,
+        dest="threshold",
+        help="set a fixed threshold for the input images to mask the computation",
+    )
+
+    parser.add_argument(
+        "-v", "--verbose", dest="verbose", action="store_true", help="verbose output"
+    )
+    parser.add_argument(
+        "-d", dest="debug", action="store_true", help="Display debug information."
+    )
+    parser.add_argument(
+        "-f",
+        "--force",
+        dest="force",
+        action="store_true",
+        help="overwrite existing files",
+    )
+    return parser
+
+
+if __name__ == "__main__":
+    main()

medpy/source/bin/medpy_binary_resampling.py

@@ -0,0 +1,313 @@
+#!/usr/bin/env python
+
+"""
+Re-samples a binary image according to a supplied voxel spacing.
+
+Copyright (C) 2013 Oskar Maier
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+import argparse
+import logging
+
+# build-in modules
+import os
+
+# third-party modules
+import numpy
+from scipy.ndimage import binary_erosion, distance_transform_edt, label, zoom
+
+# own modules
+from medpy.core import Logger
+from medpy.filter import resample
+from medpy.io import header, load, save
+from medpy.utilities import argparseu
+
+# information
+__author__ = "Oskar Maier"
+__version__ = "r0.1.0, 2014-11-25"
+__email__ = "oskar.maier@googlemail.com"
+__status__ = "Release"
+__description__ = """
+Re-samples a binary image according to a supplied voxel spacing.
+
+For an optimal results without outliers or holes in the case of up-sampling, the required
+number of additional slices is added using shape based interpolation. All down-sampling
+and the remaining small up-sampling operations are then executed with a nearest
+neighbourhood interpolation of a chosen order.
+
+BSpline is used for interpolation. A order between 0 and 5 can be selected. The default
+is 0 (= nearest neighbour). In some rare case an order of 1 (= linear) might be
+necessary.
+
+Note that the pixel data type of the input image is treated as binary.
+
+Copyright (C) 2013 Oskar Maier
+This program comes with ABSOLUTELY NO WARRANTY; This is free software,
+and you are welcome to redistribute it under certain conditions; see
+the LICENSE file or <http://www.gnu.org/licenses/> for details.
+"""
+
+
+# code
+def main():
+    parser = getParser()
+    args = getArguments(parser)
+
+    # prepare logger
+    logger = Logger.getInstance()
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+    elif args.verbose:
+        logger.setLevel(logging.INFO)
+
+    # loading input images
+    img, hdr = load(args.input)
+    img = img.astype(numpy.bool_)
+
+    # check spacing values
+    if not len(args.spacing) == img.ndim:
+        parser.error(
+            "The image has {} dimensions, but {} spacing parameters have been supplied.".format(
+                img.ndim, len(args.spacing)
+            )
+        )
+
+    # check if output image exists
+    if not args.force:
+        if os.path.exists(args.output):
+            parser.error("The output image {} already exists.".format(args.output))
+
+    logger.debug("target voxel spacing: {}".format(args.spacing))
+
+    # determine number of required complete slices for up-sampling
+    vs = header.get_pixel_spacing(hdr)
+    rcss = [
+        int(y // x - 1) for x, y in zip(args.spacing, vs)
+    ]  # TODO: For option b, remove the - 1; better: no option b, since I am rounding later anyway
+
+    # remove negatives and round up to next even number
+    rcss = [x if x > 0 else 0 for x in rcss]
+    rcss = [x if 0 == x % 2 else x + 1 for x in rcss]
+    logger.debug("intermediate slices to add per dimension: {}".format(rcss))
+
+    # for each dimension requiring up-sampling, from the highest down, perform shape based slice interpolation
+    logger.info("Adding required slices using shape based interpolation.")
+    for dim, rcs in enumerate(rcss):
+        if rcs > 0:
+            logger.debug(
+                "adding {} intermediate slices to dimension {}".format(rcs, dim)
+            )
+            img = shape_based_slice_interpolation(img, dim, rcs)
+            logger.debug("resulting new image shape: {}".format(img.shape))
+
+    # compute and set new voxel spacing
+    nvs = [x / (y + 1.0) for x, y in zip(vs, rcss)]
+    header.set_pixel_spacing(hdr, nvs)
+    logger.debug("intermediate voxel spacing: {}".format(nvs))
+
+    # interpolate with nearest neighbour
+    logger.info("Re-sampling the image with a b-spline order of {}.".format(args.order))
+    img, hdr = resample(img, hdr, args.spacing, args.order, mode="nearest")
+
+    # saving the resulting image
+    save(img, args.output, hdr, args.force)
+
+
+def shape_based_slice_interpolation(img, dim, nslices):
+    """
+    Adds `nslices` slices between all slices of the binary image `img` along dimension
+    `dim` respecting the original slice values to be situated in the middle of each
+    slice. Extrapolation situations are handled by simple repeating.
+
+    Interpolation of new slices is performed using shape based interpolation.
+
+    Parameters
+    ----------
+    img : array_like
+        A n-dimensional image.
+    dim : int
+        The dimension along which to add slices.
+    nslices : int
+        The number of slices to add. Must be an even number.
+
+    Returns
+    -------
+    out : ndarray
+        The re-sampled image.
+    """
+    # check arguments
+    if not 0 == nslices % 2:
+        raise ValueError("nslices must be an even number")
+
+    out = None
+    slicer = [slice(None)] * img.ndim
+    chunk_full_shape = list(img.shape)
+    chunk_full_shape[dim] = nslices + 2
+
+    for sl1, sl2 in zip(numpy.rollaxis(img, dim)[:-1], numpy.rollaxis(img, dim)[1:]):
+        if 0 == numpy.count_nonzero(sl1) and 0 == numpy.count_nonzero(sl2):
+            chunk = numpy.zeros(chunk_full_shape, dtype=numpy.bool_)
+        else:
+            chunk = shape_based_slice_insertation_object_wise(sl1, sl2, dim, nslices)
+        if out is None:
+            out = numpy.delete(chunk, -1, dim)
+        else:
+            out = numpy.concatenate((out, numpy.delete(chunk, -1, dim)), dim)
+
+    slicer[dim] = numpy.newaxis
+    out = numpy.concatenate((out, sl2[tuple(slicer)]), dim)
+
+    slicer[dim] = slice(0, 1)
+    for _ in range(nslices // 2):
+        out = numpy.concatenate((img[tuple(slicer)], out), dim)
+    slicer[dim] = slice(-1, None)
+    for _ in range(nslices // 2):
+        out = numpy.concatenate((out, img[tuple(slicer)]), dim)
+
+    return out
+
+
+def shape_based_slice_insertation_object_wise(sl1, sl2, dim, nslices, order=3):
+    """
+    Wrapper to apply `shape_based_slice_insertation()` for each binary object
+    separately to ensure correct extrapolation behaviour.
+    """
+    out = None
+    sandwich = numpy.concatenate((sl1[numpy.newaxis], sl2[numpy.newaxis]), 0)
+    label_image, n_labels = label(sandwich)
+    for lid in range(1, n_labels + 1):
+        _sl1, _sl2 = label_image == lid
+        _out = shape_based_slice_insertation(_sl1, _sl2, dim, nslices, order=3)
+        if out is None:
+            out = _out
+        else:
+            out |= _out
+    return out
+
+
+def shape_based_slice_insertation(sl1, sl2, dim, nslices, order=3):
+    """
+    Insert `nslices` new slices between `sl1` and `sl2` along dimension `dim` using shape
+    based binary interpolation.
+
+    Extrapolation is handled adding `nslices`/2 step-wise eroded copies of the last slice
+    in each direction.
+
+    Parameters
+    ----------
+    sl1 : array_like
+        First slice. Treated as binary data.
+    sl2 : array_like
+        Second slice. Treated as binary data.
+    dim : int
+        The new dimension along which to add the new slices.
+    nslices : int
+        The number of slices to add.
+    order : int
+        The b-spline interpolation order for re-sampling the distance maps.
+
+    Returns
+    -------
+    out : ndarray
+        A binary image of size `sl1`.shape() extend by `nslices`+2 along the new
+        dimension `dim`. The border slices are the original slices `sl1` and `sl2`.
+    """
+    sl1 = sl1.astype(numpy.bool_)
+    sl2 = sl2.astype(numpy.bool_)
+
+    # extrapolation through erosion
+    if 0 == numpy.count_nonzero(sl1):
+        slices = [sl1]
+        for _ in range(nslices / 2):
+            slices.append(numpy.zeros_like(sl1))
+        for i in range(1, nslices / 2 + nslices % 2 + 1)[::-1]:
+            slices.append(binary_erosion(sl2, iterations=i))
+        slices.append(sl2)
+        return numpy.rollaxis(numpy.asarray(slices), 0, dim + 1)
+        # return numpy.asarray([sl.T for sl in slices]).T
+    elif 0 == numpy.count_nonzero(sl2):
+        slices = [sl1]
+        for i in range(1, nslices / 2 + 1):
+            slices.append(binary_erosion(sl1, iterations=i))
+        for _ in range(0, nslices / 2 + nslices % 2):
+            slices.append(numpy.zeros_like(sl2))
+        slices.append(sl2)
+        return numpy.rollaxis(numpy.asarray(slices), 0, dim + 1)
+        # return numpy.asarray([sl.T for sl in slices]).T
+
+    # interpolation shape based
+    # note: distance_transform_edt shows strange behaviour for ones-arrays
+    dt1 = distance_transform_edt(~sl1) - distance_transform_edt(sl1)
+    dt2 = distance_transform_edt(~sl2) - distance_transform_edt(sl2)
+
+    slicer = [slice(None)] * dt1.ndim
+    slicer = slicer[:dim] + [numpy.newaxis] + slicer[dim:]
+    out = numpy.concatenate((dt1[tuple(slicer)], dt2[tuple(slicer)]), axis=dim)
+    zoom_factors = [1] * dt1.ndim
+    zoom_factors = zoom_factors[:dim] + [(nslices + 2) / 2.0] + zoom_factors[dim:]
+    out = zoom(out, zoom_factors, order=order)
+
+    return out <= 0
+
+
+def getArguments(parser):
+    "Provides additional validation of the arguments collected by argparse."
+    args = parser.parse_args()
+    if args.order < 0 or args.order > 5:
+        parser.error("The order has to be a number between 0 and 5.")
+    return args
+
+
+def getParser():
+    "Creates and returns the argparse parser object."
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        description=__description__,
+    )
+    parser.add_argument("input", help="the input image")
+    parser.add_argument("output", help="the output image")
+    parser.add_argument(
+        "spacing",
+        type=argparseu.sequenceOfFloatsGt,
+        help="the desired voxel spacing in colon-separated values, e.g. 1.2,1.2,5.0",
+    )
+    parser.add_argument(
+        "-o",
+        "--order",
+        type=int,
+        default=0,
+        dest="order",
+        help="the bspline order, default is 0 (= nearest neighbour)",
+    )
+
+    parser.add_argument(
+        "-v", "--verbose", dest="verbose", action="store_true", help="verbose output"
+    )
+    parser.add_argument(
+        "-d", dest="debug", action="store_true", help="Display debug information."
+    )
+    parser.add_argument(
+        "-f",
+        "--force",
+        dest="force",
+        action="store_true",
+        help="overwrite existing files",
+    )
+    return parser
+
+
+if __name__ == "__main__":
+    main()

medpy/source/bin/medpy_convert.py

@@ -0,0 +1,107 @@
+#!/usr/bin/env python
+
+"""
+Convert an image from one format into another.
+
+Copyright (C) 2013 Oskar Maier
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+# build-in modules
+import argparse
+import logging
+
+# own modules
+from medpy.core import Logger
+from medpy.io import load, save
+
+# third-party modules
+
+# path changes
+
+
+# information
+__author__ = "Oskar Maier"
+__version__ = "r0.1.1, 2012-05-25"
+__email__ = "oskar.maier@googlemail.com"
+__status__ = "Release"
+__description__ = """
+                  Convert an image from one format into another. The image type is
+                  determined by the file suffixes.
+
+                  Copyright (C) 2013 Oskar Maier
+                  This program comes with ABSOLUTELY NO WARRANTY; This is free software,
+                  and you are welcome to redistribute it under certain conditions; see
+                  the LICENSE file or <http://www.gnu.org/licenses/> for details.
+                  """
+
+
+# code
+def main():
+    args = getArguments(getParser())
+
+    # prepare logger
+    logger = Logger.getInstance()
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+    elif args.verbose:
+        logger.setLevel(logging.INFO)
+
+    # load input image
+    data_input, header_input = load(args.input)
+
+    # eventually empty data
+    if args.empty:
+        data_input.fill(False)
+
+    # save resulting volume
+    save(data_input, args.output, header_input, args.force)
+
+    logger.info("Successfully terminated.")
+
+
+def getArguments(parser):
+    "Provides additional validation of the arguments collected by argparse."
+    return parser.parse_args()
+
+
+def getParser():
+    "Creates and returns the argparse parser object."
+    parser = argparse.ArgumentParser(description=__description__)
+    parser.add_argument("input", help="Source volume.")
+    parser.add_argument("output", help="Target volume.")
+    parser.add_argument(
+        "-e",
+        dest="empty",
+        action="store_true",
+        help="Instead of copying the voxel data, create an empty copy conserving all meta-data if possible.",
+    )
+    parser.add_argument(
+        "-v", dest="verbose", action="store_true", help="Display more information."
+    )
+    parser.add_argument(
+        "-d", dest="debug", action="store_true", help="Display debug information."
+    )
+    parser.add_argument(
+        "-f",
+        dest="force",
+        action="store_true",
+        help="Silently override existing output images.",
+    )
+    return parser
+
+
+if __name__ == "__main__":
+    main()

medpy/source/bin/medpy_create_empty_volume_by_example.py

@@ -0,0 +1,101 @@
+#!/usr/bin/env python
+
+"""
+Creates an empty volume with the same attributes as the passes example image.
+
+Copyright (C) 2013 Oskar Maier
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>."""
+
+# build-in modules
+import argparse
+import logging
+
+# third-party modules
+import numpy
+
+# own modules
+from medpy.core import Logger
+from medpy.io import load, save
+
+# path changes
+
+
+# information
+__author__ = "Oskar Maier"
+__version__ = "r0.1.0, 2012-08-24"
+__email__ = "oskar.maier@googlemail.com"
+__status__ = "Release"
+__description__ = """
+                  Creates an empty volume with the same attributes as the passes example image.
+
+                  Copyright (C) 2013 Oskar Maier
+                  This program comes with ABSOLUTELY NO WARRANTY; This is free software,
+                  and you are welcome to redistribute it under certain conditions; see
+                  the LICENSE file or <http://www.gnu.org/licenses/> for details.
+                  """
+
+
+# code
+def main():
+    args = getArguments(getParser())
+
+    # prepare logger
+    logger = Logger.getInstance()
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+    elif args.verbose:
+        logger.setLevel(logging.INFO)
+
+    # loading input image
+    input_data, input_header = load(args.example)
+
+    # create empty volume with same attributes
+    output_data = numpy.zeros(input_data.shape, dtype=input_data.dtype)
+
+    # save resulting image
+    save(output_data, args.output, input_header, args.force)
+
+    logger.info("Successfully terminated.")
+
+
+def getArguments(parser):
+    "Provides additional validation of the arguments collected by argparse."
+    return parser.parse_args()
+
+
+def getParser():
+    "Creates and returns the argparse parser object."
+    parser = argparse.ArgumentParser(
+        description=__description__, formatter_class=argparse.RawTextHelpFormatter
+    )
+    parser.add_argument("example", help="The example volume.")
+    parser.add_argument("output", help="Target volume.")
+    parser.add_argument(
+        "-v", dest="verbose", action="store_true", help="Display more information."
+    )
+    parser.add_argument(
+        "-d", dest="debug", action="store_true", help="Display debug information."
+    )
+    parser.add_argument(
+        "-f",
+        dest="force",
+        action="store_true",
+        help="Silently override existing output images.",
+    )
+    return parser
+
+
+if __name__ == "__main__":
+    main()

medpy/source/bin/medpy_dicom_slices_to_volume.py

@@ -0,0 +1,105 @@
+#!/usr/bin/env python
+
+"""
+Converts a collection of DICOM slices into a proper image volume.
+
+Copyright (C) 2013 Oskar Maier
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>."""
+
+# build-in modules
+import argparse
+import logging
+
+# own modules
+from medpy.core import Logger
+from medpy.io import load, save
+
+# third-party modules
+
+# path changes
+
+
+# information
+__author__ = "Oskar Maier"
+__version__ = "r0.2.1, 2012-06-13"
+__email__ = "oskar.maier@googlemail.com"
+__status__ = "Release"
+__description__ = """
+                  Converts a collection of DICOM slices (a DICOM series) into a proper
+                  image volume. Note that this operation does not preserve header
+                  information.
+
+                  Copyright (C) 2013 Oskar Maier
+                  This program comes with ABSOLUTELY NO WARRANTY; This is free software,
+                  and you are welcome to redistribute it under certain conditions; see
+                  the LICENSE file or <http://www.gnu.org/licenses/> for details.
+                  """
+
+
+# code
+def main():
+    args = getArguments(getParser())
+
+    # prepare logger
+    logger = Logger.getInstance()
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+    elif args.verbose:
+        logger.setLevel(logging.INFO)
+
+    img, hdr = load(args.input)
+
+    if args.spacing:
+        print("{}".format(hdr.get_voxel_spacing()))
+        return 0
+
+    logger.debug("Resulting shape is {}.".format(img.shape))
+
+    # save resulting volume
+    save(img, args.output, hdr, args.force)
+
+    logger.info("Successfully terminated.")
+
+
+def getArguments(parser):
+    "Provides additional validation of the arguments collected by argparse."
+    return parser.parse_args()
+
+
+def getParser():
+    "Creates and returns the argparse parser object."
+    parser = argparse.ArgumentParser(description=__description__)
+    parser.add_argument("input", help="Source folder.")
+    parser.add_argument("output", help="Target volume.")
+    parser.add_argument(
+        "-s", dest="spacing", action="store_true", help="Just print spacing and exit."
+    )
+    parser.add_argument(
+        "-v", dest="verbose", action="store_true", help="Display more information."
+    )
+    parser.add_argument(
+        "-d", dest="debug", action="store_true", help="Display debug information."
+    )
+    parser.add_argument(
+        "-f",
+        dest="force",
+        action="store_true",
+        help="Silently override existing output images.",
+    )
+    return parser
+
+
+if __name__ == "__main__":
+    main()

medpy/source/bin/medpy_dicom_to_4D.py

@@ -0,0 +1,171 @@
+#!/usr/bin/env python
+
+"""
+Takes a dicom folder, loads the contained slices and saves them as a proper 4D volume.
+
+Copyright (C) 2013 Oskar Maier
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>."""
+
+# build-in modules
+import argparse
+import logging
+
+# third-party modules
+import numpy
+
+# own modules
+from medpy.core import Logger
+from medpy.core.exceptions import ArgumentError
+from medpy.io import load, save
+
+# path changes
+
+
+# information
+__author__ = "Oskar Maier"
+__version__ = "d0.2.0, 2012-05-25"
+__email__ = "oskar.maier@googlemail.com"
+__status__ = "Development"
+__description__ = """
+                  Takes a dicom folder, loads the contained slices and saves them as a proper 4D volume.
+                  The supplied target dimension parameter determines the dimension along which to split the
+                  original image and the consecutive slices parameter determines the offset after which to
+                  split.
+
+                  A typical use-case are DICOM images, which often come with the temporal and third spatial
+                  dimension stacked on top of each other.
+                  Let us assume a (5000, 200, 190) 3D image. In reality this file contains a number of 50
+                  volume of 100x200x190, which each represent a point in time. More concretely, always 50
+                  slices of the first dimension show the transformation of a 2D image in time. Then occurs
+                  a visible jump, when the view changes in space from the 50th to the 51th slice. The
+                  following 50 slices are the temporal transformation of this new spatial slice and then
+                  occur another jump, and so on.
+
+                  Calling this script with a target dimension of 0 (meaning the first dimension of the
+                  image containing the 5000 slices) and a consecutive slices parameter of 50 (which is used
+                  to tell how many consecutive slices belong together), will result in a 4D image of the
+                  shape (100, 50, 200, 190) containing the spatial volumes separated by an additional time
+                  dimension.
+
+                  Copyright (C) 2013 Oskar Maier
+                  This program comes with ABSOLUTELY NO WARRANTY; This is free software,
+                  and you are welcome to redistribute it under certain conditions; see
+                  the LICENSE file or <http://www.gnu.org/licenses/> for details.
+                  """
+
+
+# code
+def main():
+    args = getArguments(getParser())
+
+    # prepare logger
+    logger = Logger.getInstance()
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+    elif args.verbose:
+        logger.setLevel(logging.INFO)
+
+    data_3d, _ = load(args.input)
+
+    # check parameters
+    if args.dimension >= data_3d.ndim or args.dimension < 0:
+        raise ArgumentError(
+            "The image has only {} dimensions. The supplied target dimension {} exceeds this number.".format(
+                data_3d.ndim, args.dimension
+            )
+        )
+    if not 0 == data_3d.shape[args.dimension] % args.offset:
+        raise ArgumentError(
+            "The number of slices {} in the target dimension {} of the image shape {} is not dividable by the supplied number of consecutive slices {}.".format(
+                data_3d.shape[args.dimension],
+                args.dimension,
+                data_3d.shape,
+                args.offset,
+            )
+        )
+
+    # prepare empty target volume
+    volumes_3d = data_3d.shape[args.dimension] / args.offset
+    shape_4d = list(data_3d.shape)
+    shape_4d[args.dimension] = volumes_3d
+    data_4d = numpy.zeros([args.offset] + shape_4d, dtype=data_3d.dtype)
+
+    logger.debug(
+        "Separating {} slices into {} 3D volumes of thickness {}.".format(
+            data_3d.shape[args.dimension], volumes_3d, args.offset
+        )
+    )
+
+    # iterate over 3D image and create sub volumes which are then added to the 4d volume
+    for idx in range(args.offset):
+        # collect the slices
+        for sl in range(volumes_3d):
+            idx_from = [slice(None), slice(None), slice(None)]
+            idx_from[args.dimension] = slice(
+                idx + sl * args.offset, idx + sl * args.offset + 1
+            )
+            idx_to = [slice(None), slice(None), slice(None)]
+            idx_to[args.dimension] = slice(sl, sl + 1)
+            # print 'Slice {} to {}.'.format(idx_from, idx_to)
+            data_4d[idx][tuple(idx_to)] = data_3d[tuple(idx_from)]
+
+    # flip dimensions such that the newly created is the last
+    data_4d = numpy.swapaxes(data_4d, 0, 3)
+
+    # save resulting 4D volume
+    save(data_4d, args.output, False, args.force)
+
+    logger.info("Successfully terminated.")
+
+
+def getArguments(parser):
+    "Provides additional validation of the arguments collected by argparse."
+    return parser.parse_args()
+
+
+def getParser():
+    "Creates and returns the argparse parser object."
+    parser = argparse.ArgumentParser(
+        description=__description__, formatter_class=argparse.RawTextHelpFormatter
+    )
+    parser.add_argument("input", help="Source directory.")
+    parser.add_argument("output", help="Target volume.")
+    parser.add_argument(
+        "dimension",
+        type=int,
+        help="The dimension in which to perform the cut (starting from 0).",
+    )
+    parser.add_argument(
+        "offset",
+        type=int,
+        help="How many consecutive slices belong together before a shift occurs. / The offset between the volumes.",
+    )
+    parser.add_argument(
+        "-v", dest="verbose", action="store_true", help="Display more information."
+    )
+    parser.add_argument(
+        "-d", dest="debug", action="store_true", help="Display debug information."
+    )
+    parser.add_argument(
+        "-f",
+        dest="force",
+        action="store_true",
+        help="Silently override existing output images.",
+    )
+    return parser
+
+
+if __name__ == "__main__":
+    main()

medpy/source/bin/medpy_diff.py

@@ -0,0 +1,124 @@
+#!/usr/bin/env python
+
+"""
+Compares the pixel values of two images and gives a measure of the difference.
+
+Copyright (C) 2013 Oskar Maier
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>."""
+
+import argparse
+import logging
+
+# build-in modules
+import sys
+from functools import reduce
+
+# third-party modules
+import numpy
+
+# own modules
+from medpy.core import Logger
+from medpy.io import load
+
+# path changes
+
+
+# information
+__author__ = "Oskar Maier"
+__version__ = "r0.1.0, 2012-05-25"
+__email__ = "oskar.maier@googlemail.com"
+__status__ = "Release"
+__description__ = """
+                  Compares the pixel values of two images and gives a measure of the difference.
+
+                  Also compares the dtype and shape.
+
+                  Copyright (C) 2013 Oskar Maier
+                  This program comes with ABSOLUTELY NO WARRANTY; This is free software,
+                  and you are welcome to redistribute it under certain conditions; see
+                  the LICENSE file or <http://www.gnu.org/licenses/> for details.
+                  """
+
+
+# code
+def main():
+    args = getArguments(getParser())
+
+    # prepare logger
+    logger = Logger.getInstance()
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+    elif args.verbose:
+        logger.setLevel(logging.INFO)
+
+    # load input image1
+    data_input1, _ = load(args.input1)
+
+    # load input image2
+    data_input2, _ = load(args.input2)
+
+    # compare dtype and shape
+    if not data_input1.dtype == data_input2.dtype:
+        print("Dtype differs: {} to {}".format(data_input1.dtype, data_input2.dtype))
+    if not data_input1.shape == data_input2.shape:
+        print("Shape differs: {} to {}".format(data_input1.shape, data_input2.shape))
+        print(
+            "The voxel content of images of different shape can not be compared. Exiting."
+        )
+        sys.exit(-1)
+
+    # compare image data
+    voxel_total = reduce(lambda x, y: x * y, data_input1.shape)
+    voxel_difference = len((data_input1 != data_input2).nonzero()[0])
+    if not 0 == voxel_difference:
+        print(
+            "Voxel differ: {} of {} total voxels".format(voxel_difference, voxel_total)
+        )
+        print(
+            "Max difference: {}".format(numpy.absolute(data_input1 - data_input2).max())
+        )
+    else:
+        print("No other difference.")
+
+    logger.info("Successfully terminated.")
+
+
+def getArguments(parser):
+    "Provides additional validation of the arguments collected by argparse."
+    return parser.parse_args()
+
+
+def getParser():
+    "Creates and returns the argparse parser object."
+    parser = argparse.ArgumentParser(description=__description__)
+    parser.add_argument("input1", help="Source volume one.")
+    parser.add_argument("input2", help="Source volume two.")
+    parser.add_argument(
+        "-v", dest="verbose", action="store_true", help="Display more information."
+    )
+    parser.add_argument(
+        "-d", dest="debug", action="store_true", help="Display debug information."
+    )
+    parser.add_argument(
+        "-f",
+        dest="force",
+        action="store_true",
+        help="Silently override existing output images.",
+    )
+    return parser
+
+
+if __name__ == "__main__":
+    main()

medpy/source/bin/medpy_extract_contour.py

@@ -0,0 +1,188 @@
+#!/usr/bin/env python
+
+"""
+Convert a binary volume into a surface contour.
+
+Copyright (C) 2013 Oskar Maier
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+# build-in modules
+import argparse
+import logging
+import math
+
+# third-party modules
+import numpy
+from scipy.ndimage import binary_dilation, binary_erosion, generate_binary_structure
+
+# own modules
+from medpy.core import Logger
+from medpy.io import load, save
+
+# path changes
+
+
+# information
+__author__ = "Oskar Maier"
+__version__ = "r0.1.1, 2014-06-04"
+__email__ = "oskar.maier@googlemail.com"
+__status__ = "Release"
+__description__ = """
+                  Converts a binary volume into a surface contour. In the case of an even
+                  contour width, the surface of the volume will correspond with the
+                  middle of the contour line. In the case of an odd contour width, the
+                  contour will be shifted by one voxel towards the inside of the volume.
+
+                  In the case of 3D volumes, the contours result in shells, which might
+                  not be desired, as they do not visualize well in 2D views. With the
+                  '--dimension' argument, a dimension along which to extract the contours
+                  can be supplied.
+
+                  Copyright (C) 2013 Oskar Maier
+                  This program comes with ABSOLUTELY NO WARRANTY; This is free software,
+                  and you are welcome to redistribute it under certain conditions; see
+                  the LICENSE file or <http://www.gnu.org/licenses/> for details.
+                  """
+
+
+# code
+def main():
+    args = getArguments(getParser())
+
+    # prepare logger
+    logger = Logger.getInstance()
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+    elif args.verbose:
+        logger.setLevel(logging.INFO)
+
+    # load input image
+    data_input, header_input = load(args.input)
+
+    # treat as binary
+    data_input = data_input.astype(numpy.bool_)
+
+    # check dimension argument
+    if args.dimension and (
+        not args.dimension >= 0 or not args.dimension < data_input.ndim
+    ):
+        argparse.ArgumentError(
+            args.dimension,
+            "Invalid dimension of {} supplied. Image has only {} dimensions.".format(
+                args.dimension, data_input.ndim
+            ),
+        )
+
+    # compute erosion and dilation steps
+    erosions = int(math.ceil(args.width / 2.0))
+    dilations = int(math.floor(args.width / 2.0))
+    logger.debug(
+        "Performing {} erosions and {} dilations to achieve a contour of width {}.".format(
+            erosions, dilations, args.width
+        )
+    )
+
+    # erode, dilate and compute contour
+    if not args.dimension:
+        eroded = (
+            binary_erosion(data_input, iterations=erosions)
+            if not 0 == erosions
+            else data_input
+        )
+        dilated = (
+            binary_dilation(data_input, iterations=dilations)
+            if not 0 == dilations
+            else data_input
+        )
+        data_output = numpy.logical_xor(dilated, eroded)
+    else:
+        slicer = [slice(None)] * data_input.ndim
+        bs_slicer = [slice(None)] * data_input.ndim
+        data_output = numpy.zeros_like(data_input)
+        for sl in range(data_input.shape[args.dimension]):
+            slicer[args.dimension] = slice(sl, sl + 1)
+            bs_slicer[args.dimension] = slice(1, 2)
+            bs = generate_binary_structure(data_input.ndim, 1)
+
+            eroded = (
+                binary_erosion(
+                    data_input[tuple(slicer)],
+                    structure=bs[tuple(bs_slicer)],
+                    iterations=erosions,
+                )
+                if not 0 == erosions
+                else data_input[tuple(slicer)]
+            )
+            dilated = (
+                binary_dilation(
+                    data_input[tuple(slicer)],
+                    structure=bs[tuple(bs_slicer)],
+                    iterations=dilations,
+                )
+                if not 0 == dilations
+                else data_input[tuple(slicer)]
+            )
+            data_output[tuple(slicer)] = numpy.logical_xor(dilated, eroded)
+    logger.debug(
+        "Contour image contains {} contour voxels.".format(
+            numpy.count_nonzero(data_output)
+        )
+    )
+
+    # save resulting volume
+    save(data_output, args.output, header_input, args.force)
+
+    logger.info("Successfully terminated.")
+
+
+def getArguments(parser):
+    "Provides additional validation of the arguments collected by argparse."
+    args = parser.parse_args()
+    if args.width <= 0:
+        raise argparse.ArgumentError(
+            args.width, "The contour width must be a positive number."
+        )
+    return args
+
+
+def getParser():
+    "Creates and returns the argparse parser object."
+    parser = argparse.ArgumentParser(description=__description__)
+    parser.add_argument("input", help="Source volume.")
+    parser.add_argument("output", help="Target volume.")
+    parser.add_argument(
+        "-w", "--width", dest="width", type=int, default=1, help="Width of the contour."
+    )
+    parser.add_argument(
+        "--dimension", type=int, help="Extract contours only along this dimension."
+    )
+    parser.add_argument(
+        "-v", dest="verbose", action="store_true", help="Display more information."
+    )
+    parser.add_argument(
+        "-d", dest="debug", action="store_true", help="Display debug information."
+    )
+    parser.add_argument(
+        "-f",
+        dest="force",
+        action="store_true",
+        help="Silently override existing output images.",
+    )
+    return parser
+
+
+if __name__ == "__main__":
+    main()

medpy/source/bin/medpy_extract_min_max.py

@@ -0,0 +1,127 @@
+#!/usr/bin/env python
+
+"""
+Extracts and displays the min/max values of a number of images.
+
+Copyright (C) 2013 Oskar Maier
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+# build-in modules
+import argparse
+import logging
+import os
+import sys
+
+# own modules
+from medpy.core import Logger
+from medpy.io import load
+
+# third-party modules
+
+# path changes
+
+
+# information
+__author__ = "Oskar Maier"
+__version__ = "r0.2, 2011-12-13"
+__email__ = "oskar.maier@googlemail.com"
+__status__ = "Release"
+__description__ = """
+                  Extracts and displays the min/max values of a number of images
+                  and prints the results to the stdout in csv format.
+
+                  Copyright (C) 2013 Oskar Maier
+                  This program comes with ABSOLUTELY NO WARRANTY; This is free software,
+                  and you are welcome to redistribute it under certain conditions; see
+                  the LICENSE file or <http://www.gnu.org/licenses/> for details.
+                  """
+
+
+# code
+def main():
+    # parse cmd arguments
+    parser = getParser()
+    parser.parse_args()
+    args = getArguments(parser)
+
+    # prepare logger
+    logger = Logger.getInstance()
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+    elif args.verbose:
+        logger.setLevel(logging.INFO)
+
+    # build output file name
+    file_csv_name = args.csv + ".csv"
+
+    # check if output file exists
+    if not args.force:
+        if os.path.exists(file_csv_name):
+            logger.warning(
+                "The output file {} already exists. Skipping.".format(file_csv_name)
+            )
+            sys.exit(0)
+
+    # write header line
+    print("image;min;max\n")
+
+    # iterate over input images
+    for image in args.images:
+        # get and prepare image data
+        logger.info("Processing image {}...".format(image))
+        image_data, _ = load(image)
+
+        # count number of labels and flag a warning if they reach the ushort border
+        min_value = image_data.min()
+        max_value = image_data.max()
+
+        # count number of labels and write
+        print("{};{};{}\n".format(image.split("/")[-1], min_value, max_value))
+
+        sys.stdout.flush()
+
+    logger.info("Successfully terminated.")
+
+
+def getArguments(parser):
+    "Provides additional validation of the arguments collected by argparse."
+    return parser.parse_args()
+
+
+def getParser():
+    "Creates and returns the argparse parser object."
+    parser = argparse.ArgumentParser(description=__description__)
+
+    parser.add_argument("csv", help="The file to store the results in (\wo suffix).")
+    parser.add_argument("images", nargs="+", help="One or more images.")
+    parser.add_argument(
+        "-v", dest="verbose", action="store_true", help="Display more information."
+    )
+    parser.add_argument(
+        "-d", dest="debug", action="store_true", help="Display debug information."
+    )
+    parser.add_argument(
+        "-f",
+        dest="force",
+        action="store_true",
+        help="Silently override existing output images.",
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    main()

medpy/source/bin/medpy_extract_sub_volume.py

@@ -0,0 +1,190 @@
+#!/usr/bin/env python
+
+"""
+Extracts a sub-volume from a medical image.
+
+Copyright (C) 2013 Oskar Maier
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+import argparse
+import logging
+import os
+import sys
+
+# build-in modules
+from argparse import RawTextHelpFormatter
+
+# third-party modules
+import numpy
+
+# own modules
+from medpy.core import ArgumentError, Logger
+from medpy.io import load, save
+
+# path changes
+
+
+# information
+__author__ = "Oskar Maier"
+__version__ = "r0.3.0, 2011-12-11"
+__email__ = "oskar.maier@googlemail.com"
+__status__ = "Release"
+__description__ = """
+                  Takes a medical image of arbitrary dimensions and the dimensions
+                  of a sub-volume that lies inside the dimensions of this images.
+                  Extracts the sub-volume from the supplied image and saves it.
+
+                  The volume to be extracted is defined by its slices, the syntax is the same as
+                  for numpy array indexes (i.e. starting with zero-index, the first literal (x) of any
+                  x:y included and the second (y) excluded).
+                  E.g. '2:3,4:6' would extract the slice no. 3 in X and 5, 6 in Y direction of a 2D image.
+                  E.g. '99:199,149:199,99:249' would extract the respective slices in X,Y and Z direction of a 3D image.
+                       This could, for example, be used to extract the area of the liver form a CT scan.
+                  To keep all slices in one direction just omit the respective value:
+                  E.g. '99:199,149:199,' would work ust as example II, but extract all Z slices.
+                       Note here the trailing colon.
+
+                  Note to take into account the input images orientation when supplying the sub-volume.
+
+                  Copyright (C) 2013 Oskar Maier
+                  This program comes with ABSOLUTELY NO WARRANTY; This is free software,
+                  and you are welcome to redistribute it under certain conditions; see
+                  the LICENSE file or <http://www.gnu.org/licenses/> for details.
+                  """
+
+
+# code
+def main():
+    # parse cmd arguments
+    parser = getParser()
+    parser.parse_args()
+    args = getArguments(parser)
+
+    # prepare logger
+    logger = Logger.getInstance()
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+    elif args.verbose:
+        logger.setLevel(logging.INFO)
+
+    # check if output image exists
+    if not args.force:
+        if os.path.exists(args.output + args.image[-4:]):
+            logger.warning(
+                "The output file {} already exists. Breaking.".format(
+                    args.output + args.image[-4:]
+                )
+            )
+            exit(1)
+
+    # load images
+    image_data, image_header = load(args.image)
+
+    # check image dimensions against sub-volume dimensions
+    if len(image_data.shape) != len(args.volume):
+        logger.critical(
+            "The supplied input image is of different dimension as the sub volume requested ({} to {})".format(
+                len(image_data.shape), len(args.volume)
+            )
+        )
+        raise ArgumentError(
+            "The supplied input image is of different dimension as the sub volume requested ({} to {})".format(
+                len(image_data.shape), len(args.volume)
+            )
+        )
+
+    # execute extraction of the sub-area
+    logger.info("Extracting sub-volume...")
+    index = [slice(x[0], x[1]) for x in args.volume]
+    volume = image_data[tuple(index)]
+
+    # check if the output image contains data
+    if 0 == len(volume):
+        logger.exception(
+            "The extracted sub-volume is of zero-size. This usual means that the supplied volume coordinates and the image coordinates do not intersect. Exiting the application."
+        )
+        sys.exit(-1)
+
+    # squeeze extracted sub-volume for the case in which one dimensions has been eliminated
+    volume = numpy.squeeze(volume)
+
+    logger.debug("Extracted volume is of shape {}.".format(volume.shape))
+
+    # save results in same format as input image
+    save(volume, args.output, image_header, args.force)
+
+    logger.info("Successfully terminated.")
+
+
+def getArguments(parser):
+    "Provides additional validation of the arguments collected by argparse."
+    args = parser.parse_args()
+    # parse volume and adapt to zero-indexing
+    try:
+
+        def _to_int_or_none(string):
+            if 0 == len(string):
+                return None
+            return int(string)
+
+        def _to_int_or_none_double(string):
+            if 0 == len(string):
+                return [None, None]
+            return list(map(_to_int_or_none, string.split(":")))
+
+        args.volume = list(map(_to_int_or_none_double, args.volume.split(",")))
+        args.volume = [(x[0], x[1]) for x in args.volume]
+    except (ValueError, IndexError) as e:
+        raise ArgumentError(
+            'Maleformed volume parameter "{}", see description with -h flag.'.format(
+                args.volume
+            ),
+            e,
+        )
+
+    return args
+
+
+def getParser():
+    "Creates and returns the argparse parser object."
+    parser = argparse.ArgumentParser(
+        description=__description__, formatter_class=RawTextHelpFormatter
+    )
+
+    parser.add_argument("image", help="The source volume.")
+    parser.add_argument("output", help="The target volume.")
+    parser.add_argument(
+        "volume",
+        help="The coordinated of the sub-volume of the images that should be extracted.\nExample: 30:59,40:67,45:75 for a 3D image.\nSee -h for more information.",
+    )
+    parser.add_argument(
+        "-f",
+        dest="force",
+        action="store_true",
+        help="Set this flag to silently override files that exist.",
+    )
+    parser.add_argument(
+        "-v", dest="verbose", action="store_true", help="Display more information."
+    )
+    parser.add_argument(
+        "-d", dest="debug", action="store_true", help="Display debug information."
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    main()

medpy/source/bin/medpy_extract_sub_volume_auto.py

@@ -0,0 +1,189 @@
+#!/usr/bin/env python
+
+"""
+Automatically extracts sub-volumes from a medical image.
+
+Copyright (C) 2013 Oskar Maier
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+import argparse
+import logging
+import os
+
+# build-in modules
+from argparse import RawTextHelpFormatter
+
+# own modules
+from medpy.core import ArgumentError, Logger
+from medpy.io import load, save
+
+# third-party modules
+
+# path changes
+
+
+# information
+__author__ = "Oskar Maier"
+__version__ = "r0.2.1, 2012-05-17"
+__email__ = "oskar.maier@googlemail.com"
+__status__ = "Release"
+__description__ = """
+                  Takes a medical image of arbitrary dimensions and splits it into a
+                  number of sub-volumes along the supplied dimensions. The maximum size
+                  of each such created volume can be supplied.
+
+                  Note to take into account the input images orientation when supplying the cut dimension.
+                  Note that the image offsets are not preserved.
+
+                  Copyright (C) 2013 Oskar Maier
+                  This program comes with ABSOLUTELY NO WARRANTY; This is free software,
+                  and you are welcome to redistribute it under certain conditions; see
+                  the LICENSE file or <http://www.gnu.org/licenses/> for details.
+                  """
+
+
+# code
+def main():
+    # parse cmd arguments
+    parser = getParser()
+    parser.parse_args()
+    args = getArguments(parser)
+
+    # prepare logger
+    logger = Logger.getInstance()
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+    elif args.verbose:
+        logger.setLevel(logging.INFO)
+
+    # load input image
+    logger.info("Loading {}...".format(args.image))
+    image_data, image_header = load(args.image)
+
+    # check if supplied cut dimension is inside the input images dimensions
+    if args.dimension < 0 or args.dimension >= image_data.ndim:
+        logger.critical(
+            "The supplied cut-dimensions {} is invalid. The input image has only {} dimensions.".format(
+                args.dimension, image_data.ndim
+            )
+        )
+        raise ArgumentError(
+            "The supplied cut-dimensions {} is invalid. The input image has only {} dimensions.".format(
+                args.dimension, image_data.ndim
+            )
+        )
+
+    # prepare output filenames
+    name_output = args.output.replace("{}", "{:03d}")
+
+    # determine cut lines
+    no_sub_volumes = (
+        image_data.shape[args.dimension] / args.maxsize + 1
+    )  # int-division is desired
+    slices_per_volume = (
+        image_data.shape[args.dimension] / no_sub_volumes
+    )  # int-division is desired
+
+    # construct processing dict for each sub-volume
+    processing_array = []
+    for i in range(no_sub_volumes):
+        processing_array.append(
+            {
+                "path": name_output.format(i + 1),
+                "cut": (i * slices_per_volume, (i + 1) * slices_per_volume),
+            }
+        )
+        if no_sub_volumes - 1 == i:  # last volume has to have increased cut end
+            processing_array[i]["cut"] = (
+                processing_array[i]["cut"][0],
+                image_data.shape[args.dimension],
+            )
+
+    # construct base indexing list
+    index = [slice(None) for _ in range(image_data.ndim)]
+
+    # execute extraction of the sub-volumes
+    logger.info("Extracting sub-volumes...")
+    for dic in processing_array:
+        # check if output images exists
+        if not args.force:
+            if os.path.exists(dic["path"]):
+                logger.warning(
+                    "The output file {} already exists. Skipping this volume.".format(
+                        dic["path"]
+                    )
+                )
+                continue
+
+        # extracting sub-volume
+        index[args.dimension] = slice(dic["cut"][0], dic["cut"][1])
+        volume = image_data[tuple(index)]
+
+        logger.debug("Extracted volume is of shape {}.".format(volume.shape))
+
+        # saving sub-volume in same format as input image
+        logger.info("Saving cut {} as {}...".format(dic["cut"], dic["path"]))
+        save(volume, dic["path"], image_header, args.force)
+
+    logger.info("Successfully terminated.")
+
+
+def getArguments(parser):
+    "Provides additional validation of the arguments collected by argparse."
+    return parser.parse_args()
+
+
+def getParser():
+    "Creates and returns the argparse parser object."
+    parser = argparse.ArgumentParser(
+        description=__description__, formatter_class=RawTextHelpFormatter
+    )
+
+    parser.add_argument(
+        "image", help="An image of arbitrary dimensions that should be split."
+    )
+    parser.add_argument(
+        "output",
+        help='Output volumes. Has to include the sequence "{}" in the place where the volume number should be placed.',
+    )
+    parser.add_argument(
+        "dimension",
+        type=int,
+        help="The dimension in which direction to split (starting from 0:x).",
+    )
+    parser.add_argument(
+        "maxsize",
+        type=int,
+        help="The produced volumes will always be smaller than this size (in terms of slices in the cut-dimension).",
+    )
+    parser.add_argument(
+        "-f",
+        dest="force",
+        action="store_true",
+        help="Set this flag to silently override files that exist.",
+    )
+    parser.add_argument(
+        "-v", dest="verbose", action="store_true", help="Display more information."
+    )
+    parser.add_argument(
+        "-d", dest="debug", action="store_true", help="Display debug information."
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    main()

medpy/source/bin/medpy_extract_sub_volume_by_example.py

@@ -0,0 +1,199 @@
+#!/usr/bin/env python
+
+"""
+Extracts a sub-volume from a medical image by an example image.
+
+Copyright (C) 2013 Oskar Maier
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+import argparse
+import logging
+import os
+import sys
+
+# build-in modules
+from argparse import RawTextHelpFormatter
+
+# third-party modules
+import numpy
+
+# own modules
+from medpy.core import ArgumentError, Logger
+from medpy.io import load, save
+
+# path changes
+
+
+# information
+__author__ = "Oskar Maier"
+__version__ = "r0.2.0, 2011-12-11"
+__email__ = "oskar.maier@googlemail.com"
+__status__ = "Release"
+__description__ = """
+                  Takes a medical image of arbitrary dimensions and a binary mask
+                  image of the same dimensions. Extract the exact position of the
+                  binary mask in the binary mask image and uses these dimensions
+                  for the extraction of a sub-volume that lies inside the dimensions
+                  of the medical images.
+                  Extracts the sub-volume from the supplied image and saves it.
+
+                  Note that both images must be of the same dimensionality, otherwise an exception is thrown.
+                  Note that the input images offset is not taken into account.
+                  Note to take into account the input images orientation.
+
+                  This is a convenience script, combining the functionalities of
+                  extract_mask_position and extract_sub_volume.
+
+                  Copyright (C) 2013 Oskar Maier
+                  This program comes with ABSOLUTELY NO WARRANTY; This is free software,
+                  and you are welcome to redistribute it under certain conditions; see
+                  the LICENSE file or <http://www.gnu.org/licenses/> for details.
+                  """
+
+
+# code
+def main():
+    # parse cmd arguments
+    parser = getParser()
+    parser.parse_args()
+    args = getArguments(parser)
+
+    # prepare logger
+    logger = Logger.getInstance()
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+    elif args.verbose:
+        logger.setLevel(logging.INFO)
+
+    # load mask
+    logger.info("Loading mask {}...".format(args.mask))
+    mask_image, _ = load(args.mask)
+
+    # store mask images shape for later check against the input image
+    mask_image_shape = mask_image.shape
+
+    # extract the position of the foreground object in the mask image
+    logger.info("Extract the position of the foreground object...")
+    positions = mask_image.nonzero()
+    positions = [
+        (max(0, positions[i].min() - args.offset), positions[i].max() + 1 + args.offset)
+        for i in range(len(positions))
+    ]  # crop negative values
+    logger.debug("Extracted position is {}.".format(positions))
+
+    # load image
+    logger.info("Loading image {}...".format(args.image))
+    image_data, image_header = load(args.image)
+
+    # check if the mask image and the input image are of the same shape
+    if mask_image_shape != image_data.shape:
+        raise ArgumentError(
+            "The two input images are of different shape (mask: {} and image: {}).".format(
+                mask_image_shape, image_data.shape
+            )
+        )
+
+    # execute extraction of the sub-area
+    logger.info("Extracting sub-volume...")
+    index = tuple([slice(x[0], x[1]) for x in positions])
+    volume = image_data[index]
+
+    # check if the output image contains data
+    if 0 == len(volume):
+        logger.exception(
+            "The extracted sub-volume is of zero-size. This usual means that the mask image contained no foreground object."
+        )
+        sys.exit(0)
+
+    logger.debug("Extracted volume is of shape {}.".format(volume.shape))
+
+    # get base origin of the image
+    origin_base = numpy.array([0] * image_data.ndim)  # for backwards compatibility
+
+    # modify the volume offset to imitate numpy behavior (e.g. wrap negative values)
+    offset = numpy.array([x[0] for x in positions])
+    for i in range(0, len(offset)):
+        if None == offset[i]:
+            offset[i] = 0
+    offset[offset < 0] += numpy.array(image_data.shape)[offset < 0]  # wrap around
+    offset[offset < 0] = 0  # set negative to zero
+
+    # calculate final new origin
+    origin = origin_base + offset
+
+    logger.debug(
+        "Final origin created as {} + {} = {}.".format(origin_base, offset, origin)
+    )
+
+    # save results in same format as input image
+    logger.info("Saving extracted volume...")
+    save(volume, args.output, image_header, args.force)
+
+    logger.info("Successfully terminated.")
+
+
+def getArguments(parser):
+    "Provides additional validation of the arguments collected by argparse."
+    args = parser.parse_args()
+    # check output image exists if override not forced
+    if not args.force:
+        if os.path.exists(args.output + args.image[-4:]):
+            raise ArgumentError(
+                "The supplied output file {} already exists. Run -f/force flag to override.".format(
+                    args.output
+                )
+            )
+
+    return args
+
+
+def getParser():
+    "Creates and returns the argparse parser object."
+    parser = argparse.ArgumentParser(
+        description=__description__, formatter_class=RawTextHelpFormatter
+    )
+
+    parser.add_argument("image", help="The input image.")
+    parser.add_argument("output", help="The resulting sub-volume.")
+    parser.add_argument(
+        "mask", help="A mask image containing a single foreground object (non-zero)."
+    )
+    parser.add_argument(
+        "-o",
+        "--offset",
+        dest="offset",
+        default=0,
+        type=int,
+        help="Set an offset by which the extracted sub-volume size should be increased in all directions.",
+    )
+    parser.add_argument(
+        "-f",
+        dest="force",
+        action="store_true",
+        help="Set this flag to silently override files that exist.",
+    )
+    parser.add_argument(
+        "-v", dest="verbose", action="store_true", help="Display more information."
+    )
+    parser.add_argument(
+        "-d", dest="debug", action="store_true", help="Display debug information."
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    main()

medpy/source/bin/medpy_fit_into_shape.py

@@ -0,0 +1,143 @@
+#!/usr/bin/env python
+
+"""
+Fit an existing image into a new shape.
+
+Copyright (C) 2013 Oskar Maier
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+import argparse
+import logging
+
+# build-in modules
+import os
+
+# third-party modules
+import numpy
+
+# own modules
+from medpy.core import Logger
+from medpy.io import load, save
+from medpy.utilities import argparseu
+
+# information
+__author__ = "Oskar Maier"
+__version__ = "r0.1.0, 2014-11-25"
+__email__ = "oskar.maier@googlemail.com"
+__status__ = "Release"
+__description__ = """
+Fit an existing image into a new shape.
+
+If larger, the original image is placed centered in all dimensions. If smaller,
+it is cut equally at all sides.
+
+Copyright (C) 2013 Oskar Maier
+This program comes with ABSOLUTELY NO WARRANTY; This is free software,
+and you are welcome to redistribute it under certain conditions; see
+the LICENSE file or <http://www.gnu.org/licenses/> for details.
+"""
+
+
+# code
+def main():
+    parser = getParser()
+    args = getArguments(parser)
+
+    # prepare logger
+    logger = Logger.getInstance()
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+    elif args.verbose:
+        logger.setLevel(logging.INFO)
+
+    # loading input images
+    img, hdr = load(args.input)
+
+    # check shape dimensionality
+    if not len(args.shape) == img.ndim:
+        parser.error(
+            "The image has {} dimensions, but {} shape parameters have been supplied.".format(
+                img.ndim, len(args.shape)
+            )
+        )
+
+    # check if output image exists
+    if not args.force and os.path.exists(args.output):
+        parser.error("The output image {} already exists.".format(args.output))
+
+    # compute required cropping and extention
+    slicers_cut = []
+    slicers_extend = []
+    for dim in range(len(img.shape)):
+        slicers_cut.append(slice(None))
+        slicers_extend.append(slice(None))
+        if args.shape[dim] != img.shape[dim]:
+            difference = abs(img.shape[dim] - args.shape[dim])
+            cutoff_left = difference / 2
+            cutoff_right = difference / 2 + difference % 2
+            if args.shape[dim] > img.shape[dim]:
+                slicers_extend[-1] = slice(cutoff_left, -1 * cutoff_right)
+            else:
+                slicers_cut[-1] = slice(cutoff_left, -1 * cutoff_right)
+
+    # crop original image
+    img = img[tuple(slicers_cut)]
+
+    # create output image and place input image centered
+    out = numpy.zeros(args.shape, img.dtype)
+    out[tuple(slicers_extend)] = img
+
+    # saving the resulting image
+    save(out, args.output, hdr, args.force)
+
+
+def getArguments(parser):
+    "Provides additional validation of the arguments collected by argparse."
+    return parser.parse_args()
+
+
+def getParser():
+    "Creates and returns the argparse parser object."
+    parser = argparse.ArgumentParser(
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        description=__description__,
+    )
+    parser.add_argument("input", help="the input image")
+    parser.add_argument("output", help="the output image")
+    parser.add_argument(
+        "shape",
+        type=argparseu.sequenceOfIntegersGt,
+        help="the desired shape in colon-separated values, e.g. 255,255,32",
+    )
+
+    parser.add_argument(
+        "-v", "--verbose", dest="verbose", action="store_true", help="verbose output"
+    )
+    parser.add_argument(
+        "-d", dest="debug", action="store_true", help="Display debug information."
+    )
+    parser.add_argument(
+        "-f",
+        "--force",
+        dest="force",
+        action="store_true",
+        help="overwrite existing files",
+    )
+    return parser
+
+
+if __name__ == "__main__":
+    main()

medpy/source/bin/medpy_gradient.py

@@ -0,0 +1,120 @@
+#!/usr/bin/env python
+
+"""
+Executes gradient magnitude filter over images.
+
+Copyright (C) 2013 Oskar Maier
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+# build-in modules
+import argparse
+import logging
+
+# third-party modules
+import numpy
+from scipy.ndimage import generic_gradient_magnitude, prewitt
+
+from medpy.core import Logger
+
+# own modules
+from medpy.io import load, save
+
+# path changes
+
+
+# information
+__author__ = "Oskar Maier"
+__version__ = "r0.2.0, 2011-12-12"
+__email__ = "oskar.maier@googlemail.com"
+__status__ = "Release"
+__description__ = """
+                  Creates a height map of the input images using the gradient magnitude
+                  filter.
+                  The pixel type of the resulting image will be float.
+
+                  Copyright (C) 2013 Oskar Maier
+                  This program comes with ABSOLUTELY NO WARRANTY; This is free software,
+                  and you are welcome to redistribute it under certain conditions; see
+                  the LICENSE file or <http://www.gnu.org/licenses/> for details.
+                  """
+
+
+# code
+def main():
+    # parse cmd arguments
+    parser = getParser()
+    parser.parse_args()
+    args = getArguments(parser)
+
+    # prepare logger
+    logger = Logger.getInstance()
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+    elif args.verbose:
+        logger.setLevel(logging.INFO)
+
+    # laod input image
+    data_input, header_input = load(args.input)
+
+    #    # check if output image exists
+    #    if not args.force:
+    #        if os.path.exists(image_gradient_name):
+    #            logger.warning('The output image {} already exists. Skipping this step.'.format(image_gradient_name))
+    #            continue
+
+    # prepare result image
+    data_output = numpy.zeros(data_input.shape, dtype=numpy.float32)
+
+    # apply the gradient magnitude filter
+    logger.info("Computing the gradient magnitude with Prewitt operator...")
+    generic_gradient_magnitude(
+        data_input, prewitt, output=data_output
+    )  # alternative to prewitt is sobel
+
+    # save resulting mask
+    save(data_output, args.output, header_input, args.force)
+
+    logger.info("Successfully terminated.")
+
+
+def getArguments(parser):
+    "Provides additional validation of the arguments collected by argparse."
+    return parser.parse_args()
+
+
+def getParser():
+    "Creates and returns the argparse parser object."
+    parser = argparse.ArgumentParser(description=__description__)
+    parser.add_argument("input", help="Source volume.")
+    parser.add_argument("output", help="Target volume.")
+    parser.add_argument(
+        "-v", dest="verbose", action="store_true", help="Display more information."
+    )
+    parser.add_argument(
+        "-d", dest="debug", action="store_true", help="Display debug information."
+    )
+    parser.add_argument(
+        "-f",
+        dest="force",
+        action="store_true",
+        help="Silently override existing output images.",
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    main()

medpy/source/bin/medpy_graphcut_label.py

@@ -0,0 +1,212 @@
+#!/usr/bin/env python
+
+"""
+Execute a graph cut on a region image based on some foreground and background markers.
+
+Copyright (C) 2013 Oskar Maier
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+import argparse
+import logging
+import os
+
+# build-in modules
+from argparse import RawTextHelpFormatter
+
+# third-party modules
+import numpy
+
+from medpy import filter, graphcut
+
+# own modules
+from medpy.core import ArgumentError, Logger
+from medpy.graphcut.wrapper import split_marker
+from medpy.io import load, save
+
+# path changes
+
+
+# information
+__author__ = "Oskar Maier"
+__version__ = "r0.4.4, 2012-03-16"
+__email__ = "oskar.maier@googlemail.com"
+__status__ = "Release"
+__description__ = """
+                  Perform a binary graph cut using Boykov's max-flow/min-cut algorithm.
+
+                  This implementation does only compute a boundary term and does not use
+                  any regional term. The desired boundary term can be selected via the
+                  --boundary argument. Depending on the selected term, an additional
+                  image has to be supplied as badditional.
+
+                  In the case of the stawiaski boundary term, this is the gradient image.
+                  In the case of the difference of means, it is the original image.
+
+                  Furthermore the algorithm requires the region map of the original
+                  image and an integer image with foreground and background markers.
+
+                  Additionally a filename for the created binary mask marking foreground
+                  and background has to be supplied.
+
+                  Note that the input images must be of the same dimensionality,
+                  otherwise an exception is thrown.
+                  Note to take into account the input images orientation.
+                  Note that the quality of the resulting segmentations depends also on
+                  the quality of the supplied markers.
+
+                  Copyright (C) 2013 Oskar Maier
+                  This program comes with ABSOLUTELY NO WARRANTY; This is free software,
+                  and you are welcome to redistribute it under certain conditions; see
+                  the LICENSE file or <http://www.gnu.org/licenses/> for details.
+                  """
+
+
+# code
+def main():
+    # parse cmd arguments
+    parser = getParser()
+    parser.parse_args()
+    args = getArguments(parser)
+
+    # prepare logger
+    logger = Logger.getInstance()
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+    elif args.verbose:
+        logger.setLevel(logging.INFO)
+
+    # check if output image exists
+    if not args.force:
+        if os.path.exists(args.output):
+            logger.warning(
+                "The output image {} already exists. Exiting.".format(args.output)
+            )
+            exit(-1)
+
+    # select boundary term
+    if args.boundary == "stawiaski":
+        boundary_term = graphcut.energy_label.boundary_stawiaski
+        logger.info("Selected boundary term: stawiaski")
+    else:
+        boundary_term = graphcut.energy_label.boundary_difference_of_means
+        logger.info("Selected boundary term: difference of means")
+
+    # load input images
+    region_image_data, reference_header = load(args.region)
+    badditional_image_data, _ = load(args.badditional)
+    markers_image_data, _ = load(args.markers)
+
+    # split marker image into fg and bg images
+    fgmarkers_image_data, bgmarkers_image_data = split_marker(markers_image_data)
+
+    # check if all images dimensions are the same
+    if not (
+        badditional_image_data.shape
+        == region_image_data.shape
+        == fgmarkers_image_data.shape
+        == bgmarkers_image_data.shape
+    ):
+        logger.critical("Not all of the supplied images are of the same shape.")
+        raise ArgumentError("Not all of the supplied images are of the same shape.")
+
+    # recompute the label ids to start from id = 1
+    logger.info("Relabel input image...")
+    region_image_data = filter.relabel(region_image_data)
+
+    # generate graph
+    logger.info("Preparing graph...")
+    gcgraph = graphcut.graph_from_labels(
+        region_image_data,
+        fgmarkers_image_data,
+        bgmarkers_image_data,
+        boundary_term=boundary_term,
+        boundary_term_args=(badditional_image_data),
+    )  # second is directedness of graph , 0)
+
+    logger.info("Removing images that are not longer required from memory...")
+    del fgmarkers_image_data
+    del bgmarkers_image_data
+    del badditional_image_data
+
+    # execute min-cut
+    logger.info("Executing min-cut...")
+    maxflow = gcgraph.maxflow()
+    logger.debug("Maxflow is {}".format(maxflow))
+
+    # apply results to the region image
+    logger.info("Applying results...")
+    mapping = [0]  # no regions with id 1 exists in mapping, entry used as padding
+    mapping.extend(
+        [
+            0 if gcgraph.termtype.SINK == gcgraph.what_segment(int(x) - 1) else 1
+            for x in numpy.unique(region_image_data)
+        ]
+    )
+    region_image_data = filter.relabel_map(region_image_data, mapping)
+
+    # save resulting mask
+    save(
+        region_image_data.astype(numpy.bool_), args.output, reference_header, args.force
+    )
+
+    logger.info("Successfully terminated.")
+
+
+def getArguments(parser):
+    "Provides additional validation of the arguments collected by argparse."
+    return parser.parse_args()
+
+
+def getParser():
+    "Creates and returns the argparse parser object."
+    parser = argparse.ArgumentParser(
+        description=__description__, formatter_class=RawTextHelpFormatter
+    )
+
+    parser.add_argument(
+        "badditional",
+        help="The additional image required by the boundary term. See there for details.",
+    )
+    parser.add_argument("region", help="The region image of the image to segment.")
+    parser.add_argument(
+        "markers",
+        help="Binary image containing the foreground (=1) and background (=2) markers.",
+    )
+    parser.add_argument("output", help="The output image containing the segmentation.")
+    parser.add_argument(
+        "--boundary",
+        default="stawiaski",
+        help="The boundary term to use. Note that difference of means (means) requires the original image, while stawiaski requires the gradient image of the original image to be passed to badditional.",
+        choices=["means", "stawiaski"],
+    )
+    parser.add_argument(
+        "-f",
+        dest="force",
+        action="store_true",
+        help="Set this flag to silently override files that exist.",
+    )
+    parser.add_argument(
+        "-v", dest="verbose", action="store_true", help="Display more information."
+    )
+    parser.add_argument(
+        "-d", dest="debug", action="store_true", help="Display debug information."
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    main()

medpy/source/bin/medpy_graphcut_label_bgreduced.py

@@ -0,0 +1,271 @@
+#!/usr/bin/env python
+
+"""
+Execute a graph cut on a region image based on some foreground and background markers.
+
+Copyright (C) 2013 Oskar Maier
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+import argparse
+import itertools
+import logging
+import os
+
+# build-in modules
+from argparse import RawTextHelpFormatter
+
+# third-party modules
+import numpy
+from scipy import ndimage
+
+from medpy import filter, graphcut
+
+# own modules
+from medpy.core import ArgumentError, Logger
+from medpy.graphcut.wrapper import split_marker
+from medpy.io import load, save
+
+# path changes
+
+
+# information
+__author__ = "Oskar Maier"
+__version__ = "r0.3.4, 2012-03-16"
+__email__ = "oskar.maier@googlemail.com"
+__status__ = "Release"
+__description__ = """
+                  !Modified version of original GC label, as reduces the volume sizes
+                  using the background markers.
+
+                  Perform a binary graph cut using Boykov's max-flow/min-cut algorithm.
+
+                  This implementation does only compute a boundary term and does not use
+                  any regional term. The desired boundary term can be selected via the
+                  --boundary argument. Depending on the selected term, an additional
+                  image has to be supplied as badditional.
+
+                  In the case of the stawiaski boundary term, this is the gradient image.
+                  In the case of the difference of means, it is the original image.
+
+                  Furthermore the algorithm requires the region map of the original
+                  image, a binary image with foreground markers and a binary
+                  image with background markers.
+
+                  Additionally a filename for the created binary mask marking foreground
+                  and background has to be supplied.
+
+                  Note that the input images must be of the same dimensionality,
+                  otherwise an exception is thrown.
+                  Note to take into account the input images orientation.
+                  Note that the quality of the resulting segmentations depends also on
+                  the quality of the supplied markers.
+
+                  Copyright (C) 2013 Oskar Maier
+                  This program comes with ABSOLUTELY NO WARRANTY; This is free software,
+                  and you are welcome to redistribute it under certain conditions; see
+                  the LICENSE file or <http://www.gnu.org/licenses/> for details.
+                  """
+
+
+# code
+def main():
+    # parse cmd arguments
+    parser = getParser()
+    parser.parse_args()
+    args = getArguments(parser)
+
+    # prepare logger
+    logger = Logger.getInstance()
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+    elif args.verbose:
+        logger.setLevel(logging.INFO)
+
+    # check if output image exists
+    if not args.force:
+        if os.path.exists(args.output):
+            logger.warning(
+                "The output image {} already exists. Exiting.".format(args.output)
+            )
+            exit(-1)
+
+    # load input images
+    region_image_data, reference_header = load(args.region)
+    markers_image_data, _ = load(args.markers)
+    gradient_image_data, _ = load(args.gradient)
+
+    # split marker image into fg and bg images
+    logger.info("Extracting foreground and background markers...")
+    fgmarkers_image_data, bgmarkers_image_data = split_marker(markers_image_data)
+
+    # check if all images dimensions are the same shape
+    if not (
+        gradient_image_data.shape
+        == region_image_data.shape
+        == fgmarkers_image_data.shape
+        == bgmarkers_image_data.shape
+    ):
+        logger.critical("Not all of the supplied images are of the same shape.")
+        raise ArgumentError("Not all of the supplied images are of the same shape.")
+
+    # collect cut objects
+    cut_xy = __get_bg_bounding_pipe(bgmarkers_image_data)
+
+    # cut volumes
+    old_size = region_image_data.shape
+    gradient_image_data = gradient_image_data[cut_xy]
+    region_image_data = region_image_data[cut_xy]
+    fgmarkers_image_data = fgmarkers_image_data[cut_xy]
+    bgmarkers_image_data = bgmarkers_image_data[cut_xy]
+
+    # recompute the label ids to start from id = 1
+    logger.info("Relabel input image...")
+    region_image_data = filter.relabel(region_image_data)
+
+    # generate graph
+    logger.info("Preparing graph...")
+    gcgraph = graphcut.graph_from_labels(
+        region_image_data,
+        fgmarkers_image_data,
+        bgmarkers_image_data,
+        boundary_term=graphcut.energy_label.boundary_stawiaski,
+        boundary_term_args=(gradient_image_data),
+    )  # second is directedness of graph , 0)
+
+    logger.info("Removing images that are not longer required from memory...")
+    del fgmarkers_image_data
+    del bgmarkers_image_data
+    del gradient_image_data
+
+    # execute min-cut
+    logger.info("Executing min-cut...")
+    maxflow = gcgraph.maxflow()
+    logger.debug("Maxflow is {}".format(maxflow))
+
+    # apply results to the region image
+    logger.info("Applying results...")
+    mapping = [0]  # no regions with id 1 exists in mapping, entry used as padding
+    mapping.extend(
+        [
+            0 if gcgraph.termtype.SINK == gcgraph.what_segment(int(x) - 1) else 1
+            for x in numpy.unique(region_image_data)
+        ]
+    )
+    region_image_data = filter.relabel_map(region_image_data, mapping)
+
+    # generating final image by increasing the size again
+    output_image_data = numpy.zeros(old_size, dtype=numpy.bool_)
+    output_image_data[cut_xy] = region_image_data
+
+    # save resulting mask
+    save(output_image_data, args.output, reference_header, args.force)
+
+    logger.info("Successfully terminated.")
+
+
+def __get_bg_bounding_pipe(bgmarkers):
+    # constants
+    xdim = 0
+    ydim = 1
+
+    # compute biggest bb in direction
+    bb = __xd_iterator_pass_on(bgmarkers, (xdim, ydim), __extract_bbox)
+
+    slicer = [slice(None)] * bgmarkers.ndim
+    slicer[xdim] = bb[0]
+    slicer[ydim] = bb[1]
+
+    return tuple(slicer)
+
+
+def __xd_iterator_pass_on(arr, view, fun):
+    """
+    Like xd_iterator, but the fun return values are always passed on to the next and only the last returned.
+    """
+    # create list of iterations
+    iterations = [
+        [None] if dim in view else list(range(arr.shape[dim]))
+        for dim in range(arr.ndim)
+    ]
+
+    # iterate, create slicer, execute function and collect results
+    passon = None
+    for indices in itertools.product(*iterations):
+        slicer = [
+            slice(None) if idx is None else slice(idx, idx + 1) for idx in indices
+        ]
+        passon = fun(numpy.squeeze(arr[tuple(slicer)]), passon)
+
+    return passon
+
+
+def __extract_bbox(arr, bb_old):
+    "Extracts the bounding box of an binary objects hole (assuming only one in existence)."
+    hole = ndimage.binary_fill_holes(arr) - arr
+    bb_list = ndimage.find_objects(ndimage.binary_dilation(hole, iterations=1))
+    if 0 == len(bb_list):
+        return bb_old
+    else:
+        bb = bb_list[0]
+
+    if not bb_old:
+        return list(bb)
+
+    for i in range(len(bb_old)):
+        bb_old[i] = slice(
+            min(bb_old[i].start, bb[i].start), max(bb_old[i].stop, bb[i].stop)
+        )
+    return tuple(bb_old)
+
+
+def getArguments(parser):
+    "Provides additional validation of the arguments collected by argparse."
+    return parser.parse_args()
+
+
+def getParser():
+    "Creates and returns the argparse parser object."
+    parser = argparse.ArgumentParser(
+        description=__description__, formatter_class=RawTextHelpFormatter
+    )
+
+    parser.add_argument(
+        "gradient", help="The gradient magnitude image of the image to segment."
+    )
+    parser.add_argument("region", help="The region image of the image to segment.")
+    parser.add_argument(
+        "markers",
+        help="Binary image containing the foreground (=1) and background (=2) markers.",
+    )
+    parser.add_argument("output", help="The output image containing the segmentation.")
+    parser.add_argument(
+        "-f",
+        dest="force",
+        action="store_true",
+        help="Set this flag to silently override files that exist.",
+    )
+    parser.add_argument(
+        "-v", dest="verbose", action="store_true", help="Display more information."
+    )
+    parser.add_argument(
+        "-d", dest="debug", action="store_true", help="Display debug information."
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    main()

medpy/source/bin/medpy_graphcut_label_w_regional.py

@@ -0,0 +1,249 @@
+#!/usr/bin/env python
+
+"""
+Execute a graph cut on a region image based on some foreground and background markers.
+
+Copyright (C) 2013 Oskar Maier
+
+This program is free software: you can redistribute it and/or modify
+it under the terms of the GNU General Public License as published by
+the Free Software Foundation, either version 3 of the License, or
+(at your option) any later version.
+
+This program is distributed in the hope that it will be useful,
+but WITHOUT ANY WARRANTY; without even the implied warranty of
+MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+GNU General Public License for more details.
+
+You should have received a copy of the GNU General Public License
+along with this program.  If not, see <http://www.gnu.org/licenses/>.
+"""
+
+import argparse
+import logging
+import os
+
+# build-in modules
+from argparse import RawTextHelpFormatter
+
+# third-party modules
+import numpy
+
+from medpy import filter, graphcut
+
+# own modules
+from medpy.core import ArgumentError, Logger
+from medpy.graphcut.wrapper import split_marker
+from medpy.io import load, save
+
+# path changes
+
+
+# information
+__author__ = "Oskar Maier"
+__version__ = "d0.2.1, 2012-07-31"
+__email__ = "oskar.maier@googlemail.com"
+__status__ = "Development"
+__description__ = """
+                  Perform a binary graph cut using Boykov's max-flow/min-cut algorithm.
+
+                  This implementation does not only compute a boundary term but also a
+                  regional term which. The only available implementation up till now is
+                  the use of an atalas (i.e. a probability image of float values). The
+                  pixel values have to lie between 0 and 1, whereas 1 denounces a sure
+                  probability that the object is situated at this position.  The desired
+                  boundary term can be selected via the --boundary argument. Depending on
+                  the selected term, an additional image has to be supplied as badditional.
+
+                  In the case of the stawiaski boundary term, this is the gradient image.
+                  In the case of the difference of means, it is the original image.
+
+                  Furthermore the algorithm requires the region map of the original
+                  image and an integer image with foreground and background markers.
+
+                  Additionally a filename for the created binary mask marking foreground
+                  and background has to be supplied.
+
+                  Note that the input images must be of the same dimensionality,
+                  otherwise an exception is thrown.
+                  Note to take into account the input images orientation.
+                  Note that the quality of the resulting segmentations depends also on
+                  the quality of the supplied markers.
+
+                  Copyright (C) 2013 Oskar Maier
+                  This program comes with ABSOLUTELY NO WARRANTY; This is free software,
+                  and you are welcome to redistribute it under certain conditions; see
+                  the LICENSE file or <http://www.gnu.org/licenses/> for details.
+                  """
+
+
+# code
+def main():
+    # parse cmd arguments
+    parser = getParser()
+    parser.parse_args()
+    args = getArguments(parser)
+
+    # prepare logger
+    logger = Logger.getInstance()
+    if args.debug:
+        logger.setLevel(logging.DEBUG)
+    elif args.verbose:
+        logger.setLevel(logging.INFO)
+
+    # check if output image exists
+    if not args.force:
+        if os.path.exists(args.output):
+            logger.warning(
+                "The output image {} already exists. Exiting.".format(args.output)
+            )
+            exit(-1)
+
+    # select boundary term
+    if args.boundary == "stawiaski":
+        boundary_term = graphcut.energy_label.boundary_stawiaski
+        logger.info("Selected boundary term: stawiaski")
+    else:
+        boundary_term = graphcut.energy_label.boundary_difference_of_means
+        logger.info("Selected boundary term: difference of means")
+
+    # select regional term
+    if args.regional == "atlas":
+        regional_term = graphcut.energy_label.regional_atlas
+    else:
+        regional_term = None
+
+    # load input images
+    region_image_data, reference_header = load(args.region)
+    markers_image_data, _ = load(args.markers)
+
+    # loading and splitting the marker image
+    fgmarkers_image_data, bgmarkers_image_data = split_marker(markers_image_data)
+
+    badditional_image_data, _ = load(args.badditional)
+
+    if "radditional" in args:
+        radditional_image_data, _ = load(args.radditional)
+    else:
+        radditional_image_data = False
+
+    # check if all images dimensions are the same
+    if not (
+        badditional_image_data.shape
+        == region_image_data.shape
+        == fgmarkers_image_data.shape
+        == bgmarkers_image_data.shape
+    ):
+        logger.critical("Not all of the supplied images are of the same shape.")
+        raise ArgumentError("Not all of the supplied images are of the same shape.")
+    if not bool == type(radditional_image_data):
+        if not (badditional_image_data.shape == radditional_image_data.shape):
+            logger.critical("Not all of the supplied images are of the same shape.")
+            raise ArgumentError("Not all of the supplied images are of the same shape.")
+
+    # recompute the label ids to start from id = 1
+    logger.info("Relabel input image...")
+    region_image_data = filter.relabel(region_image_data)
+
+    # generate graph
+    logger.info("Preparing graph...")
+    gcgraph = graphcut.graph_from_labels(
+        region_image_data,
+        fgmarkers_image_data,
+        bgmarkers_image_data,
+        regional_term=regional_term,
+        boundary_term=boundary_term,
+        regional_term_args=(radditional_image_data, args.alpha),
+        boundary_term_args=(badditional_image_data),
+    )  # second (optional) parameter is directedness of graph , 0)
+
+    logger.info("Removing images that are not longer required from memory...")
+    del fgmarkers_image_data
+    del bgmarkers_image_data
+    del radditional_image_data
+    del badditional_image_data
+
+    # execute min-cut
+    logger.info("Executing min-cut...")
+    maxflow = gcgraph.maxflow()
+    logger.debug("Maxflow is {}".format(maxflow))
+
+    # apply results to the region image
+    logger.info("Applying results...")
+    mapping = [0]  # no regions with id 1 exists in mapping, entry used as padding
+    mapping.extend(
+        [
+            0 if gcgraph.termtype.SINK == gcgraph.what_segment(int(x) - 1) else 1
+            for x in numpy.unique(region_image_data)
+        ]
+    )
+    region_image_data = filter.relabel_map(region_image_data, mapping)
+
+    # save resulting mask
+    save(
+        region_image_data.astype(numpy.bool_), args.output, reference_header, args.force
+    )
+
+    logger.info("Successfully terminated.")
+
+
+def getArguments(parser):
+    "Provides additional validation of the arguments collected by argparse."
+    return parser.parse_args()
+
+
+def getParser():
+    "Creates and returns the argparse parser object."
+    parser = argparse.ArgumentParser(
+        description=__description__, formatter_class=RawTextHelpFormatter
+    )
+
+    parser.add_argument(
+        "badditional",
+        help="The additional image required by the boundary term. See there for details.",
+    )
+    parser.add_argument("region", help="The region image of the image to segment.")
+    parser.add_argument(
+        "markers",
+        help="Binary image containing the foreground (=1) and background (=2) markers.",
+    )
+    parser.add_argument("output", help="The output image containing the segmentation.")
+    parser.add_argument(
+        "--boundary",
+        default="stawiaski",
+        help="The boundary term to use. Note that difference of means (means) requires the original image, while stawiaski requires the gradient image of the original image to be passed to badditional.",
+        choices=["means", "stawiaski"],
+    )
+    parser.add_argument(
+        "--regional",
+        default="none",
+        help="The regional term to use. Note that the atlas requires to provide an atlas image.",
+        choices=["none", "atlas"],
+    )
+    parser.add_argument(
+        "--radditional",
+        help="The additional image required by the regional term. See there for details.",
+    )
+    parser.add_argument(
+        "--alpha",
+        type=float,
+        help="The weight of the regional term compared to the boundary term.",
+    )
+    parser.add_argument(
+        "-f",
+        dest="force",
+        action="store_true",
+        help="Set this flag to silently override files that exist.",
+    )
+    parser.add_argument(
+        "-v", dest="verbose", action="store_true", help="Display more information."
+    )
+    parser.add_argument(
+        "-d", dest="debug", action="store_true", help="Display debug information."
+    )
+
+    return parser
+
+
+if __name__ == "__main__":
+    main()