opencv-bioimage-analysis

# OpenCV — Bio-image Computer Vision ## Overview OpenCV (cv2) provides optimized C++-backed image processing routines for preprocessing, segmentation, feature extraction, and video analysis of biological images. In life sciences, OpenCV is used for fluorescence image enhancement (background subtraction, CLAHE), morphological segmentation (watershed, contour detection), brightfield cell detection, and real-time microscopy stream processing. Unlike scikit-image (which emphasizes scientific measurement), OpenCV prioritizes computational speed and video support — making it ideal for preprocessing pipelines and real-time imaging applications. ## When to Use - Preprocessing fluorescence or brightfield images: background subtraction, CLAHE, Gaussian/median blur - Detecting cell contours, blobs, or edges without deep learning (classical methods) - Processing video streams from live-cell imaging microscopes in real-time - Template matching for finding repeated structures (organelles, crystals, patterns) - Applying morphological operations (erosion, dilation, opening, closing) for mask refinement - Computing optical flow between video frames for cell tracking - Use **scikit-image** instead for scientific morphometry, regionprops, and scientific image I/O (TIFF metadata) - Use **Cellpose** or **StarDist** instead for deep-learning cell segmentation on fluorescence images ## Prerequisites - **Python packages**: `opencv-python`, `numpy`, `matplotlib` - **Optional**: `opencv-contrib-...