# MRI Vision Core (Version 0.1) A clean Python/OpenCV MRI image-processing application with a Streamlit UI. **Disclaimer:** Research and educational prototype. Not for medical diagnosis or clinical decision-making. ## Purpose Provides a shared computer-vision core for image loading, OpenCV-based preprocessing, basic segmentation, and feature extraction of MRI images. ## Architecture - **mri_core/**: Core OpenCV computer vision algorithms. - `loader.py`: Handles loading PNG/JPG/JPEG into OpenCV formats. - `preprocessing.py`: Grayscaling, aspect-preserving resize, intensity normalization, CLAHE, and Gaussian denoising. - `segmentation.py`: Otsu and Adaptive thresholding with morphological cleanup. - `features.py`: Computes basic CV metrics. - `visualization.py`: Creates overlay views. - `pipeline.py`: High-level orchestration for the core components. - **app.py**: Streamlit application UI. - **tests/**: Pytest suite ensuring components function properly. ## Installation & Windows Setup 1. Create a virtual environment: ```cmd python -m venv .venv ``` 2. Activate it: ```cmd .venv\Scripts\activate ``` 3. Install dependencies: ```cmd pip install -r requirements.txt ``` ## How to Test Run the tests with: ```cmd pytest -q ``` ## How to Run Start the application: ```cmd streamlit run app.py ``` ## Current Capabilities ### Version 0.1 - OpenCV preprocessing - CLAHE enhancement - Gaussian denoising - Otsu/adaptive segmentation - segmentation visualization - basic image feature extraction - Streamlit interface - 5 automated tests Research and educational prototype. Not for medical diagnosis or clinical decision-making. ## Limitations - Version 0.1 only supports simple image formats (PNG/JPG). - Basic segmentation is NOT medically robust. - Requires CPU inference, no GPU optimizations included yet. ## Future Roadmap Future versions may add: - DICOM/NIfTI loading - ROI detection - advanced segmentation - radiomics/features - ML/DL inference - model comparison - REST API - cloud deployment # MRI-Vision-Core