IJERT-EMS
IJERT-EMS

A Modified Adaptive Fuzzy C-Means Clustering Algorithm For Brain MR Image Segmentation


A Modified Adaptive Fuzzy C-Means Clustering Algorithm For Brain MR  Image Segmentation
Authors : M. Ganesh, V. Palanisamy
Publication Date: 29-10-2012

Authors

Author(s):  M. Ganesh, V. Palanisamy

Published in:   International Journal of Engineering Research & Technology

License:  This work is licensed under a Creative Commons Attribution 4.0 International License.

Website: www.ijert.org

Volume/Issue:   Vol.1 - Issue 8 (October - 2012)

e-ISSN:   2278-0181

Abstract

Fuzzy c-means (FCM) clustering has been widely used in image segmentation. However, in spite of its computational efficiency and wide spread popularity, the FCM algorithm does not take the spatial information of pixels into consideration, and hence may result in low robustness to noise and less accurate segmentation. In this paper, a modified adaptive fuzzy c-means clustering (AFCM) algorithm is presented for fuzzy segmentation of magnetic resonance (MR) images. To estimate the intensity inhomogeneity, the global intensity is introduced into the coherent local intensity clustering algorithm and takes the local and global intensity information into account. The proposed method has been successfully applied to recorded MR images with desirable results. Our results show that the proposed AFCM algorithm can effectively segment the test images and MR images. Comparisons with other FCM approaches based on number of iterations and time complexity demonstrate the superior performance of the proposed algorithm.

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