Investigation on Dermoscopic Image Segmentation using Fuzzy Clustering Techniques

Abstract

Medical image segmentation is the most essential and crucial process in order to facilitate the characterization and visualization of the
structure of interest in medical images. This paper explains the task of segmenting skin lesions in Dermoscopy images using various Fuzzy clustering
techniques for the early diagnosis of Malignant Melanoma. The various Fuzzy clustering techniques used are Fuzzy C Means Algorithm (FCM),
Possibilistic C Means Algorithm and Hierarchical C Means Algorithm. The segmented images are compared with the ground truth image using
various parameters such as False Positive Error (FPE), False Negative Error (FNE) Coefficient of similarity, Spatial overlap and their performance is
evaluated.

Keywords: Fuzzy C Means clustering, Possibilistic C clustering, Hierarchical C Means, False Positive Error, False Negative Error, Coefficient of
similarity, Spatial overlap