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name: analysis description: "Extract features, match descriptors, measure labeled regions, trace contours, and compare images or segmentations with metrics." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Analysis

Use this sub-skill when a task asks scikit-image to turn already-loaded or preprocessed images into quantitative outputs: keypoints, descriptors, descriptor matches, labeled-region measurements, contour or surface geometry, line/profile measurements, or image/segmentation quality scores.

This route covers the stable skimage.feature, skimage.measure, and skimage.metrics analysis surface. Keep it focused on analysis results. When the task moves from matched features into geometric transform estimation, warping, or registration, hand off to ../transform-registration/SKILL.md.

Route here

• Detect or describe image features with APIs such as hog, corner_harris/corner_peaks, corner_subpix, ORB, SIFT, BRIEF, CENSURE, daisy, match_template, blob detectors, texture descriptors, or related skimage.feature helpers.
• Match descriptor arrays with match_descriptors and inspect correspondence arrays or plot_matched_features visualizations.
• Fit robust geometric models on measured point sets with ransac, LineModelND, CircleModel, or EllipseModel when the output is still an analysis result.
• Measure already-labeled objects with regionprops, regionprops_table, moments, perimeter measurements, intensity summaries, table-style outputs, spacing, and offset handling.
• Extract contours, simplified polygons, line intensity profiles, or 3D surfaces with find_contours, approximate_polygon, subdivide_polygon, profile_line, marching_cubes, and mesh_surface_area.
• Compare images, masks, point sets, or label maps with structural_similarity, peak_signal_noise_ratio, mean_squared_error, normalized_root_mse, normalized_mutual_information, adapted_rand_error, variation_of_information, and Hausdorff metrics.

Reroute

• Loading/saving images, sample datasets, dtype/range conversion, or NumPy image conventions: use ../data-io/SKILL.md.
• Color conversion, contrast/exposure adjustment, filtering, denoising, threshold selection, or restoration before analysis: use ../enhancement/SKILL.md.
• Creating masks, drawing synthetic shapes, morphology cleanup, watershed/random-walker/SLIC/snake methods, or other object partitioning: use ../segmentation-and-shapes/SKILL.md.
• Estimating or applying affine/projective/similarity transforms, warps, pyramids, phase cross-correlation, optical flow, or registration: use ../transform-registration/SKILL.md after this route has produced or validated correspondences.
• Maintainer commands, repository tests, benchmarks, or build tooling are outside the runtime analysis route.

Start fast

1. Identify the requested output: descriptor vector, keypoint coordinates, match index pairs, per-object table, contour coordinates, mesh, line profile, or scalar metric.
2. Confirm inputs are already arrays with the expected shape and range. Most feature detectors expect 2D grayscale data; HOG and SSIM can use color data when channel_axis is set.
3. For labeled-object measurement, require an integer label image where 0 is background. If the user only has a raw image or binary mask and asks how to create labels, route to segmentation first.
4. For physical measurements, carry spacing through regionprops, regionprops_table, moments, and marching_cubes; use offset with regionprops when measurements come from cropped label images.
5. For image metrics on float arrays, pass an explicit data_range; for color SSIM, pass the correct channel_axis.
6. For feature matching, return descriptor matches and corresponding point arrays. Do not own transform estimation; cross-link to transform-registration for RANSAC transform fitting, warp application, and alignment evaluation.

Output contracts to preserve

• Feature keypoints are coordinate arrays associated with the detector; descriptor arrays must keep the same row order as their keypoints.
• match_descriptors returns a (Q, 2) integer array of descriptor indices: column 0 indexes the first descriptor set and column 1 indexes the second.
• regionprops returns lazy RegionProperties objects; regionprops_table returns a pandas-compatible dictionary of columns.
• find_contours returns a list of (N, 2) arrays in image coordinate order; contours that hit an image edge can be open.
• marching_cubes returns (verts, faces, normals, values); mesh_surface_area(verts, faces) consumes the first two outputs.
• Metrics return scalar scores or small tuples. Preserve tuple ordering for segmentation metrics: adapted_rand_error returns (error, precision, recall) and variation_of_information returns (false_splits, false_merges).

References

• references/workflows.md gives copyable analysis workflows for feature extraction, descriptor matching, region measurement, contours/surfaces, and metrics.
• references/troubleshooting.md maps label-image dtype, descriptor matching, contour, spacing/offset, and data_range failures to fixes.