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Advances in Fuzzy Systems
Volume 2016, Article ID 3406406, 14 pages
http://dx.doi.org/10.1155/2016/3406406
Review Article

FCM Clustering Algorithms for Segmentation of Brain MR Images

Department of Electronics and Telecommunication, Yeshwantrao Chavan College of Engineering, Wanadongri, Hingna Road, Nagpur, Maharashtra 441110, India

Received 8 November 2015; Revised 16 February 2016; Accepted 17 February 2016

Academic Editor: Rustom M. Mamlook

Copyright © 2016 Yogita K. Dubey and Milind M. Mushrif. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The study of brain disorders requires accurate tissue segmentation of magnetic resonance (MR) brain images which is very important for detecting tumors, edema, and necrotic tissues. Segmentation of brain images, especially into three main tissue types: Cerebrospinal Fluid (CSF), Gray Matter (GM), and White Matter (WM), has important role in computer aided neurosurgery and diagnosis. Brain images mostly contain noise, intensity inhomogeneity, and weak boundaries. Therefore, accurate segmentation of brain images is still a challenging area of research. This paper presents a review of fuzzy -means (FCM) clustering algorithms for the segmentation of brain MR images. The review covers the detailed analysis of FCM based algorithms with intensity inhomogeneity correction and noise robustness. Different methods for the modification of standard fuzzy objective function with updating of membership and cluster centroid are also discussed.