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arXiv Computer Vision By Wenhao Xu, Yixian Kong, Ting Pan, Changwei Wang, Feilong Wang, Rongtao Xu

Initialization and Stopping Tolerance in CPU Dermoscopic Segmentation

Read the original on arXiv Computer Vision →

The study investigates how contour initialization and stopping tolerance jointly influence active‑contour segmentation performance on dermoscopic images. Using scikit‑image’s Chan‑Vese implementation on a resized ISIC 2017 dataset, the authors compare Otsu, checkerboard, disk, and Otsu‑initialized contours under default and tighter level‑set tolerances. Results show that Otsu initialization improves mean Dice scores at default tolerance but the advantage diminishes with tighter tolerance, and that evaluation metrics can vary across lesion size strata, underscoring the need to report initial fields, tolerances, and iteration counts.

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