Entropy based optimal multilevel thresholding using cuckoo optimization algorithm

Image thresholding considered as a popular method for image segmentation. So far, many approaches have been proposed for image thresholding. Maximum entropy thresholding has been widely applied in the literature. This paper proposes a multilevel image thresholding (MECOAT) using cuckoo optimization algorithm (COA). COA is a new nature- based optimization algorithm which is inspired by a bird named cuckoo. This algorithm is based unusual egg laying and breeding of cuckoos. MECOAT tries to maximize entropy criterion. Three different algorithms are compared with MECOAT algorithm: particle swarm optimization, genetic algorithm, and bat algorithm. Experimental results indicate that MECOAT presents better results in terms of fitness value, peak signal to noise ratio (PSNR) and robustness in most cases.