scikit-image - Scientific Image Processing

scikit-image treats images as NumPy arrays. It provides a comprehensive suite of algorithms for filtering, feature detection, and object measurement, making it the standard for research-grade image analysis.

When to Use

• Preprocessing scientific images (noise reduction, contrast enhancement).
• Image segmentation (separating cells, particles, or regions of interest).
• Feature extraction (detecting edges, corners, blobs, or textures).
• Geometric transformations (rescaling, rotating, warping).
• Morphological operations (thinning, skeletonization, hole filling).
• Measuring object properties (area, perimeter, eccentricity).
• Restoring degraded images (deconvolution, inpainting).

Reference Documentation

Official docs: https://scikit-image.org/
User Guide: https://scikit-image.org/docs/stable/user_guide.html
Search patterns: skimage.filters, skimage.segmentation, skimage.feature, skimage.morphology