metadata
title: Image Quantization and CT Windowing Explorer
emoji: 🩻
colorFrom: blue
colorTo: gray
sdk: docker
app_port: 8501
pinned: false
Image Quantization and CT Windowing Explorer
An interactive Streamlit app for exploring how quantization and CT-style windowing change grayscale medical image display.
Screenshots / GIF
Main app overview
Project Summary
This app compares two related but different operations on grayscale medical images:
- Quantization reduces the number of stored gray levels and can introduce banding or reconstruction error.
- Windowing changes only how a chosen intensity range is displayed and clips values outside that range.
The interface lets users adjust bit depth and window bounds, then compare four image states, a histogram, an error map, and summary metrics side by side.
Local Run
cd medical-quantization-windowing-explorer
make setup
source .venv/bin/activate
make run
Or run without activating:
uv run streamlit run app.py
Hugging Face Space URL
For the most stable viewing experience, use the direct app URL: https://huggingkatze-ct-windowing-quantization-demo.hf.space
The Hugging Face project page is: https://huggingface.co/spaces/HuggingKatze/ct-windowing-quantization-demo
Repository Structure
app.py
processing.py
metrics.py
utils.py
sample_images/
sample_images/builtin_samples/
docs/
requirements.txt
Makefile
Dockerfile
Features
- Built-in CT-oriented examples from CT-RATE, LDCT-and-Projection-data, and RSNA PE
- Upload support for
PNG,JPG,JPEG, and single-sliceDICOM - Automatic handling of grayscale images, including 3-channel grayscale copies
- Adjustable quantization bit depth and CT-style window bounds
- Slice selection for volumetric built-in samples
- Rotation control for uploaded DICOM slices
- Four synchronized views: original, windowed, quantized, and quantized + windowed
- Histogram, squared-error map, and summary metrics (
MSE,PSNR, gray levels, entropy) - Clickable comparison arrows synchronized with the metrics selector
- In-app explanatory notes for each main control
Known Limitations
- The app is educational and not intended for clinical interpretation.
- Uploaded PNG/JPG images may already be restricted to 8-bit precision.
- The current version works on one 2D slice at a time.
- Uploaded files are limited to PNG/JPG and single-slice DICOM in the current app; NIfTI upload is not enabled in the active UI.
- The CT-RATE built-in sample is rotated 90 degrees clockwise in the app to match the expected viewing orientation.
- Public deployment should still respect the original dataset licenses and redistribution terms for bundled sample content.
Interface Guide
- Input source switches between prepared built-in examples and your own uploaded data.
- Built-in sample lets you compare different CT-oriented datasets quickly.
- Slice index appears for volumetric built-in inputs and chooses the active axial slice.
- Rotate uploaded image appears for uploaded DICOM data when the slice orientation needs correction.
- Quantization bit depth controls how many gray levels remain after uniform quantization.
- The app initializes the quantization display at 4 bits by default, unless the loaded image has a lower maximum bit depth.
- CT window preset applies common lower/upper bound pairs such as lung or mediastinal ranges.
- Window lower / upper bounds and the numeric bound inputs define the exact displayed intensity interval.
- Histogram uses the selected window bounds as reference lines and is meant to show how quantization changes the intensity distribution, rather than to faithfully plot the post-windowed display mapping.
- Visualization arrows can be clicked to choose which pair of panels should be compared.
- Metrics → Compare is synchronized with the arrow graph and updates all downstream plots.