Fuzzy-based artificial bee colony optimization for gray image segmentation
In this study, segmentation of medical images using a fuzzy artificial bee colony algorithm with a cooling schedule is created. In this study, we embed ed fuzzy inference strategy into the artificial bee colony system to construct a segmentation system named Fuzzy Artificial Bee Colony System (FABCS). A conventional FCM algorithm did not utilize the spatial information in the image. We set a local circular area with a variable radius by using a cooling schedule for each bee to search suitable cluster centers with the FCM algorithm in an image. The cluster centers can be calculated by each bee with the membership states in the FABCS and then updated iteratively for all bees in order to find near-global solution in MR image segmentation. The proposed FABCS found the cluster centers with local spatial information in stead of global pixels’ intensities. In the simulation and real medical-image segmentation results, the proposed FABCS network can reserve the segmentation performance.