Analysis of CBC and FCMC Clustering approaches for Skin Melanoma Segmentation using Dermoscopic Images

Abstract

In this paper, clustering approaches are analyzed for skin lesion segmentation using dermoscopic images. Three widely used machine learning approaches for image segmentation are Centroid-based clustering (CBC). Fuzzy C-Means Clustering (FCMC), and Expectation-Maximization (EM)–E&M step algorithm. The difference between CBC and FCMC lies in the partitioning method. The former one uses hard partitioning, and the later uses a variable degree of membership. In the EM algorithm, statistical methods are employed for distance calculation whereas, in CBC, the Euclidean distance measure is used. The segmentation results of individual clustering approaches are combined to get the refined skin lesion. Results show that the combined segmentation provides promising results for skin lesion segmentation in comparison with CBC, FCMC and EM- M step algorithm.

Clustering of Countries Based on Fertilizer Consumption

“…Ward's method identifies three distinct clusters, showcasing internal homogeneity and external heterogeneity, providing valuable insights for soil management and agricultural planning. Maniraj and Maran (2022) in their paper, clustering approaches are analyzed for skin lesion segmentation using dermoscopic images. A widely used machine learning approach for image segmentation is Centroid-based clustering (CBC).…”