Segmentation of Skin Lesions in Dermatoscopic Images Using the Total Variation Chan–Vese Model

Accurate segmentation of skin lesions in dermoscopic images is essential for the early diagnosis and treatment of skin conditions, including melanoma. This study evaluates the performance of the total variation Chan–Vese model, a level set-based segmentation method, on the HAM10000 dataset, which encompasses seven diagnostic categories. By evolving contours based on intensity differences, the total variation of the Chan–Vese model provides significant advantages in segmentation. We employed pre-processing steps such as noise removal, image enhancement, and artifact elimination to enhance accuracy. The segmentation performance was assessed using the Jaccard Index, revealing high accuracy across various lesion types and demonstrating the model’s adaptability and robustness. Despite encountering computational limitations, this study underscores the potential of the total variation Chan–Vese model in clinical skin lesion analysis.

Department

Electrical and Computer Engineering

Publisher

Springer, Singapore

Sponsor

Effat University

Copyright

CC0 1.0 Universal

cb

Book title

Lecture Notes in Networks and Systems, LNNS

Open Access

https://repository.effatuniversity.edu.sa/handle/20.500.14131/2737

DOI

10.1007/978-981-96-9242-2_37