Multilevel minimum cross entropy thresholding: A comparative study

Abstract

Multilevel thresholding method is one of the most popular techniques in image segmentation. However, the multilevel thresholding method is time-consuming, its time complexity increases exponentially with the number of threshold levels. In this paper, in order to improve the computation efficiency of the multilevel minimum cross entropy thresholding, the iterative formula of the multilevel cross entropy thresholding algorithm is proposed and compared with the modern meta-heuristic optimization algorithms. The iterative multilevel cross entropy thresholding algorithm can find the thresholds close to the global optimums with less time. The computation cost of the iterative multilevel cross entropy thresholding algorithm is linear in the number of the threshold levels. We prove the convergence of the iterative algorithm and compare the iterative multilevel cross entropy thresholding algorithm with multilevel cross entropy thresholding methods combined with the state-of-the-art meta-heuristic optimization techniques including particle swarm optimization (PSO), cuckoo search algorithm (CS), differential evolution (DE), crow search algorithm (CSA) and genetic algorithm (GA). Experimental results show that the iterative multilevel cross entropy thresholding algorithm is efficient and effective. Therefore, iterative algorithm for the multilevel cross entropy thresholding is an effective method to improve the computation efficiency.