Author(s): Maniraj S P, Sardar Maran P


DOI: 10.52711/0974-360X.2022.00807   

Address: Maniraj S P1, Sardar Maran P2
1Research Scholar, Sathyabama Institute of Science and Technology, Chennai - 600119, Tamil Nadu, India.
2Associate Professor, Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Chennai - 600119, Tamil Nadu, India.
*Corresponding Author

Published In:   Volume - 15,      Issue - 10,     Year - 2022

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.

Cite this article:
Maniraj S P, Sardar Maran P. Analysis of CBC and FCMC Clustering approaches for Skin Melanoma Segmentation using Dermoscopic Images. Research Journal of Pharmacy and Technology 2022; 15(10):4807-1. doi: 10.52711/0974-360X.2022.00807

Maniraj S P, Sardar Maran P. Analysis of CBC and FCMC Clustering approaches for Skin Melanoma Segmentation using Dermoscopic Images. Research Journal of Pharmacy and Technology 2022; 15(10):4807-1. doi: 10.52711/0974-360X.2022.00807   Available on:

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