Automatic Segmentation of Medical Images Using Fuzzy c-Means and the Genetic Algorithm. (31st January 2013)

• Record Type: Journal Article
• Authors: Jamshidi, Omid; Pilevar, Abdol Hamid
• Other Names: Watabe Hiroshi Academic Editor.
• Is Part Of: Journal of computational medicine. Volume 2013(2013)
• Publication Date: 2013-01-31
• Subjects: Medical informatics -- Periodicals; Computational intelligence -- Periodicals; Computational intelligence; Medical informatics; Periodicals; Electronic journals; 610.285
• DOI: 10.1155/2013/972970
• Languages: English
• ISSNs: 2314-5080

Abstract

Abstract : 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.