Multilevel image thresholding by using the shuffled frog-leaping optimization algorithm

In this paper, a new multilevel MCET algorithm using the shuffled frog-leaping optimization (SFLO) algorithm is proposed. The proposed image thresholding algorithm is called SFLO-based MCET algorithm. Three different methods including the exhaustive search, the honey bee mating optimization (HBMO) and the particle swarm optimization (PSO) algorithms are also implemented for comparison. The experimental results demonstrate that the proposed SFLO-based MCET algorithm can efficiently search for multiple thresholds that are very close to the optimal ones examined by the exhaustive search method. Compared with the other two thresholding methods, the needs of computation time using the SFLO-based MCET algorithm is the smallest. And further, the performance of segmentation is better than the one of PSO-based MCET algorithm, while the result of SFLO-based MCET algorithm is insignificant with respect to the HBMO-based MCET algorithms.