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Dermoscopic segmentation research examines initialization and stopping tolerance

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the impact of contour initialization and stopping tolerance on CPU-based dermoscopic segmentation performance. The study utilized the scikit-image Chan-Vese implementation on the ISIC 2017 dataset, comparing various initialization methods like Otsu thresholding, checkerboard, disk, and default settings. Results indicated that Otsu initialization improved mean Dice scores compared to checkerboard initialization at default tolerances, but tightening tolerances reduced this advantage and highlighted issues with early stopping for certain initializers. AI

RANK_REASON Research paper published on arXiv detailing a new method for image segmentation. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

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Dermoscopic segmentation research examines initialization and stopping tolerance

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Research paper published on arXiv detailing a new method for image segmentation. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Wenhao Xu, Yixian Kong, Ting Pan, Feilong Wang, Changwei Wang ·

    Initialization and Stopping Tolerance in CPU Dermoscopic Segmentation

    arXiv:2609.25685v2 Announce Type: replace Abstract: Contour initialization and numerical stopping can jointly affect the evaluation of active-contour segmentation. We examine their interaction using the open-source scikit-image Chan-Vese implementation on a resized ISIC 2017 mirr…

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