Title: A comparative study among Fuzzy C-Means variants for skin lesion segmentation

Authors: Suman Bera; Rakesh Mahata; Agnish Ghosh; Bidisha Pal; Krishna Gopal Dhal

Addresses: Department of Computer Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal, India ' Department of Computer Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal, India ' Department of Computer Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal, India ' Department of Computer Application, Midnapore College (Autonomous), Paschim Medinipur, West Bengal, India ' Department of Computer Science, Midnapore College (Autonomous), Paschim Medinipur, West Bengal, India

Abstract: Segmentation of skin lesions from dermoscopic images is crucial for the early diagnosis and prognosis of many skin disorders. Fuzzy C-Means (FCM) is a widely employed partitional clustering method that has been effectively applied for image segmentation. However, FCM exhibits many limitations, including longer computational time, estimation of cluster numbers, bad convergence, local optima trapping issues, and susceptibility to noise. Therefore, researchers developed some improved variants of FCM in the image segmentation field to overcome the said issues. This study employed seven popular improved FCM variants in the skin lesion image segmentation field and performed a rigorous comparative study among them. The segmentation results of the tested FCM variants have been evaluated visually and in terms of ground truth image-based quality metrics.

Keywords: Fuzzy C-Means; FCM; lesion segmentation; skin cancer; clustering; image segmentation.

DOI: 10.1504/IJAISC.2026.153146

International Journal of Artificial Intelligence and Soft Computing, 2026 Vol.10 No.1, pp.110 - 121

Received: 01 Oct 2025
Accepted: 24 Jan 2026
Published online: 23 Apr 2026 *

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