A New Hybrid Image Segmentation Method Based on Fuzzy C-Mean and Modified Bat Algorithm

Abstract

Magnetic resonance imaging (MRI) plays an important role in clinical diagnosis, because of that it has attracted increasing attention in recent years. The symptom of many diseases corresponds to the brain's structural variants. The detection of various diseases has became very useful through the segmentation methods. Fuzzy c-means (FCM) considers among the popular clustering algorithms for medical image segmentation. However, FCM is sensitive to the noise and falls into local optimal solution easily because of the random initialization of the cluster centers. In this research, we propose a hybrid method based on modified fuzzy bat algorithm (MFBA) and the FCM clustering algorithm named MFBAFCM. This developed approach uses the MFBA to get better initial cluster centers for the FCM algorithm by using a new fitness function, which combines intra cluster distance with fuzzy cluster validity indices. Experimental results on several MRI brain images corrupted by different levels of intensity non-uniformity and noise, show that the proposed method produced better results than the standard FCM and some other recent published works