%0 Journal Article
%T Automatic Segmentation of Medical Images Using Fuzzy c-Means and the Genetic Algorithm
%A Omid Jamshidi
%A Abdol Hamid Pilevar
%J Journal of Computational Medicine
%D 2013
%I Hindawi Publishing Corporation
%R 10.1155/2013/972970
%X 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. 1. Introduction Image segmentation is one of the difficult issues in the field of image processing. Image segmentation is the process of assigning a label to every pixel in an image so that pixels with the same label share certain visual characteristics. Many applications such as object identification, feature extraction, and object position identifications and classification require accurate image segmentation. Several methods of medical image segmentation have been proposed, such as edge based, region based, or a combination of both. The purpose of medical image segmentation is to provide a more meaningful image which can be more easily understood and analyzed. The edge-based methods use edge information in an image to determine the boundaries of objects and, hence, to form closed regions that determine different objects in an image. In some image segmentation methods, this method has been consistently used with the edge of the area for segmenting magnetic resonance imaging (MRI). Chun and Yang performed image segmentation according to the edge information [1]. In addition to edge information, they made use of a similarity measure which was obtained as the median pixel variance parameters. The method used a fuzzy validity function as well as genetic algorithms and tried to find limit and suitable search space for image segmentation. Finding the main edges and removing redundant edges were the main issues in this method. Moreover, atlas-based segmentation methods were successfully employed for different applications. For
%U http://www.hindawi.com/journals/jcm/2013/972970/