xsitk_confidence_connected — 2D `extra` op

• Data kinds: image → region

• Call: fullseye.apply(img, "xsitk_confidence_connected", a=0.5, b=0.5) (the 2-D model is one image plus two scalar knobs a,b∈[0,1])

Usage

Confidence-connected region growing (SimpleITK `ConfidenceConnected`). Uses the image center as a seed, builds an interval of 'mean +/- multiplier * standard deviation' from the mean and standard deviation of the current region, and expands the connected region while iteratively updating this interval (a more statistical way of choosing the threshold than xsitk_connected_threshold).

> The detailed description below is the original text — the summary and the headings are translated.

`a は反復回数(1+int(a*5) で 1〜6)、b は標準偏差の倍率(1.0+3.0*b` で 1〜4)を振る。初期近傍半径は 2 固定。画像中心付近に対象があることを前提とした op。

Detailed usage guide

• gallery2d_color_artistic family guide

References (sample data, literature)

• Sample-data catalog (download URLs / licences) — 2-D uses skimage.data (BSD/public domain) plus synthetic images; 3-D lists download URLs for real data sources (Stanford, PDS, …).

• Operator provenance and references — the sources of the research/methods this op family came from.

• The canonical algorithm (author, year) and its uses are named in the family usage guide above.

Runnable examples (verified samples that actually call this op)

• gallery2d_color_artistic — py -3.11 examples/gallery2d_color_artistic.py

Ops the type connects to (they accept `region` as input)

identity · reg_erode · reg_dilate · reg_open · reg_close · fill_holes · select_largest · remove_small

Same category (`extra`)

xsitk_curvature_flow · xsitk_minmax_curv_flow · xsitk_curv_aniso_diff · xsitk_laplacian_sharpen · xsitk_grayscale_fillhole · xsitk_grayscale_grindpeak · xsitk_opening_by_recon · xsitk_closing_by_recon

*Provenance: ops.py — 2D operator registry. This per-op note is generated by tools/opdocs.py md (do not hand-edit).*

xsitk_confidence_connected — 2D extra op