scikit-image-processing
Python image processing for microscopy and bioimage analysis. Read/write images, filter (Gaussian, median, LoG), segment (thresholding, watershed, active contours), measure region properties, detect features. SciPy/NumPy ecosystem. Use OpenCV for real-time video; CellPose for DL cell segmentation; napari for visualization.
npx skills add BioTender-max/awesome-bio-agent-skills --skill scikit-image-processing --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# scikit-image — Scientific Image Processing ## Overview scikit-image is a Python library for image processing in the SciPy ecosystem. It provides algorithms for reading/writing images, filtering (noise reduction, edge detection), geometric transforms, segmentation (thresholding, watershed, active contours), object measurement (area, intensity, shape descriptors), and feature detection. Images are represented as NumPy arrays, enabling seamless integration with NumPy, SciPy, matplotlib, and pandas. Widely used for fluorescence microscopy, histology, and general bioimage analysis. ## When to Use - Preprocessing fluorescence microscopy images: background subtraction, denoising, illumination correction - Segmenting cells, nuclei, or organelles using thresholding or watershed - Measuring object properties: area, perimeter, intensity statistics, shape descriptors - Applying morphological operations: erosion, dilation, opening, closing, fill holes - Detecting keypoints or local features in biological images - Converting between image formats and color spaces - Use `OpenCV` instead for real-time video processing or GPU-accelerated operations - For deep-learning cell segmentation, use `Cell
- Overview
- When to Use
- Prerequisites
- Quick Start
- Core API
- Module 1: Image I/O and Data Types
- Module 2: Filters and Preprocessing
- Module 3: Thresholding and Segmentation
- Module 4: Morphological Operations
- Module 5: Measurement and Region Properties
- Module 6: Feature Detection and Transforms
- Key Concepts
- Image Arrays and Conventions
- Common Workflows
pip install scikit-image numpy scipy matplotlib For reading proprietary microscopy formats pip install tifffile aicsimageio Verify python -c "import skimage; print(skimage.__version__)"
What does the scikit-image-processing skill do?
Python image processing for microscopy and bioimage analysis. Read/write images, filter (Gaussian, median, LoG), segment (thresholding, watershed, active contours), measure region properties, detect features. SciPy/NumPy ecosystem. Use OpenCV for real-time video; CellPose for DL cell segmentation; napari for visualization.
How do I install it?
Run `npx skills add BioTender-max/awesome-bio-agent-skills --skill scikit-image-processing --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From BioTender-max/awesome-bio-agent-skills, a repository with 135 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.