Table of Contents
Journal of Computational Medicine
Volume 2013, Article ID 972970, 7 pages
Research Article

Automatic Segmentation of Medical Images Using Fuzzy c-Means and the Genetic Algorithm

Medical Intelligence Lab, Department of Computer Engineering, Bu Ali Sina University, Hamedan, Iran

Received 14 December 2012; Revised 17 January 2013; Accepted 17 January 2013

Academic Editor: Hiroshi Watabe

Copyright © 2013 Omid Jamshidi and Abdol Hamid Pilevar. 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.


Magnetic resonance imaging (MRI) segmentation is a complex issue. This paper proposes a new method for estimating the right number of segments and automatic segmentation of human normal and abnormal MR brain images. The purpose of automatic diagnosis of the segments is to find the number of divided image areas of an image according to its entropy and with correctly diagnose of the segment of an image also increased the precision of segmentation. Regarding the fact that guessing the number of image segments and the center of segments automatically requires algorithm test many states in order to solve this problem and to have a high accuracy, we used a combination of the genetic algorithm and the fuzzy c-means (FCM) method. In this method, it has been tried to change the FCM method as a fitness function for combination of it in genetic algorithm to do the image segmentation more accurately. Our experiment shows that the proposed method has a significant improvement in the accuracy of image segmentation in comparison to similar methods.