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"""
Utility functions for neuroimaging preprocessing and analysis
"""
import numpy as np
from PIL import Image
def normalize_medical_image(image_array: np.ndarray) -> np.ndarray:
"""
Normalize medical image intensities to 0-255 range
Handles various bit depths common in medical imaging
"""
img = image_array.astype(np.float32)
# Handle different intensity ranges
if img.max() > 255:
# Likely 12-bit or 16-bit image
p1, p99 = np.percentile(img, [1, 99])
img = np.clip(img, p1, p99)
# Normalize to 0-255
img_min, img_max = img.min(), img.max()
if img_max > img_min:
img = (img - img_min) / (img_max - img_min) * 255
return img.astype(np.uint8)
def apply_window_level(image_array: np.ndarray, window: float, level: float) -> np.ndarray:
"""
Apply window/level (contrast/brightness) adjustment
Common in CT viewing
Args:
image_array: Input image
window: Window width (contrast)
level: Window center (brightness)
"""
img = image_array.astype(np.float32)
min_val = level - window / 2
max_val = level + window / 2
img = np.clip(img, min_val, max_val)
img = (img - min_val) / (max_val - min_val) * 255
return img.astype(np.uint8)
def enhance_brain_contrast(image: Image.Image) -> Image.Image:
"""
Enhance contrast specifically for brain MRI visualization
"""
img_array = np.array(image)
# Convert to grayscale if needed
if len(img_array.shape) == 3:
gray = np.mean(img_array, axis=2)
else:
gray = img_array
# Apply histogram equalization
from PIL import ImageOps
enhanced = ImageOps.equalize(Image.fromarray(gray.astype(np.uint8)))
# Convert back to RGB
enhanced_array = np.array(enhanced)
rgb_array = np.stack([enhanced_array] * 3, axis=-1)
return Image.fromarray(rgb_array)
# Common neuroimaging structure mappings
STRUCTURE_ALIASES = {
"hippocampus": ["hippocampal formation", "hippocampal", "medial temporal"],
"ventricle": ["ventricular system", "lateral ventricle", "CSF space"],
"white matter": ["WM", "cerebral white matter", "deep white matter"],
"gray matter": ["GM", "cortical gray matter", "cortex"],
"tumor": ["mass", "lesion", "neoplasm", "growth"],
"thalamus": ["thalamic", "diencephalon"],
"basal ganglia": ["striatum", "caudate", "putamen", "globus pallidus"],
}
def get_structure_aliases(structure: str) -> list:
"""Get alternative names for a neuroanatomical structure"""
structure_lower = structure.lower()
for key, aliases in STRUCTURE_ALIASES.items():
if structure_lower == key or structure_lower in aliases:
return [key] + aliases
return [structure]
# Hugging Face datasets for neuroimaging
HF_NEUROIMAGING_DATASETS = {
"brain-tumor-classification": {
"repo": "sartajbhuvaji/brain-tumor-classification",
"description": "Brain MRI scans classified by tumor type (glioma, meningioma, pituitary, no tumor)",
"image_key": "image",
"label_key": "label"
},
"brain-tumor-detection": {
"repo": "keremberke/brain-tumor-object-detection",
"description": "Brain MRI with bounding box annotations for tumors",
"image_key": "image",
"label_key": "objects"
},
"chest-xray": {
"repo": "alkzar90/NIH-Chest-X-ray-dataset",
"description": "Chest X-ray images with disease labels",
"image_key": "image",
"label_key": "labels"
}
} |