Segmentation of MR brain images using FCM improved by artificial bee colony (ABC) algorithm

Segmentation of medical images, particularly magnetic resonance images of brain is complex and it is considered as a huge challenge in image processing. Among the numerous algorithms presented in this context, the fuzzy C-mean (FCM) algorithm is widely used in MR images segmentation. Recently, researchers have introduced two new parameters in order to improve the performance of FCM algorithm, which are calculated using neural network in a complex and time consuming manner. These two parameters have been then calculated by other researchers using genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, which although it has reduced the time but no change obtained in the resulted segmentation quality. In this paper we calculate these two parameters using the artificial bee colony (ABC) algorithm aiming to both reduce the time and to reach a higher quality than that obtained by previous reports. Finally, we segment real MR images with our proposed algorithm and compare it with previous presented algorithms.