Using Buzzard Optimization Algorithm for Multilevel Thresholding of Brain CT Images

Image segmentation is the process of dividing a digital image into several parts. The segmentation goal is to simplify, or change the representation of an image into something that is both more meaningful and easier to analyze. Thresholding methods with much less complexity are still widely used compared to modern methods based on deep learning. In this paper, a new optimal multi-level thresholding algorithm for histogram-based segmentation of images is presented. The proposed algorithm compared to Particle Swarm Optimization Algorithm (PSO) and an improved version of the PSO based on multi-agent fuzzy which is called MAFPSO. In the proposed algorithm, the selection of image thresholding is done using the recently introduced buzzard optimization algorithm (BUZO). In the BUZO algorithm, the process of exploration and exploitation is achieved by defining several types of buzzard with different abilities. Multilevel segmentation is performed-using entropy as a fitness function for BUZO. Comparing the performance of BUZO algorithm with MAFPSO, and PSO for several benchmark images show 8 percent average improvement for fitness function. The quality of segmented images shows 3% in average improvement for 2-level segmented image, and shows 12% in average improvement for 5-level segmented images.