A Hybrid Method for Image Segmentation Based on Modified Bat Algorithm and Fuzzy C-Means

Abstract

Image segmentation is rapidly applied in the field of image processing. Fuzzy c-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM sensitive to the noise and, falling into local optimal solution easily, because of the random initialization of the cluster centers. To solve these problems, we proposed a hybrid method, named MFBAFCM (modified fuzzy Bat algorithm for FCM) uses the MFBA to get the initial cluster centers of FCM by using a new fitness function which combines fuzzy cluster validity indices with intra cluster distance. The MFBAFCM was evaluated on several MRI brain images corrupted by different levels of noise and intensity non- uniformity. Experiment results show that the proposed method has improves segmentation results and gives better result than the standard FCM.