Related Experiment Video
Updated: Jun 7, 2026
SCAnED - An Open-source Skin Segmentation Macro for Semi-automated Cell and Nuclei Detection in Epidermal and Dermal Skin Compartments
Published on: August 8, 2025
Modified watershed technique and post-processing for segmentation of skin lesions in dermoscopy images
Hanzheng Wang1, Randy H Moss, Xiaohe Chen
1Department of Electrical and Computer Engineering, Missouri University of Science and Technology, Rolla, MO, USA. hwqkc@mst.edu
Abstract:
In previous research, a watershed-based algorithm was shown to be useful for automatic lesion segmentation in dermoscopy images, and was tested on a set of 100 benign and malignant melanoma images with the average of three sets of dermatologist-drawn borders used as the ground truth, resulting in an overall error of 15.98%. In this study, to reduce the border detection errors, a neural network classifier was utilized to improve the first-pass watershed segmentation; a novel "edge object value (EOV) threshold" method was used to remove large light blobs near the lesion boundary; and a noise removal procedure was applied to reduce the peninsula-shaped false-positive areas. As a result, an overall error of 11.09% was achieved.
