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Thresholding is one of the simplest techniques for performing image segmentation (Pal, 1996). It is refers to separate the objects and the background of the image into compacts and well separated classes. Thresholding involved bi-level thresholding and multilevel thresholding according to the number of thresholds. Both bi-level and multilevel thresholding methods can be separated into parametric and nonparametric approaches. In nonparametric approach, thresholds are selected by optimizing (maximizing or minimizing) some criterion functions defined from images such as the between class variance (Otsu, 1979) and entropy (Kapur, Sahoo and Wong, 1985). The parametric approach is based on a statistical model of the pixel grey level distribution. Generally, a set of parameters that best fits the model is derived using least square estimation (Kittler and Illingworth, 1986).
A large number of thresholding methods have been proposed in the literature in order to perform bi-level thresholding and most of them are easily extendable to multilevel thresholding. However, the computational time will increase sharply when the number of thresholds becomes too high (Yin, 2007; Ouadfel and Meshoul, 2014).
During the last years, nature-inspired metaheuristics gained the attention of the researchers to solve multilevel thresholding problem. In this field, we find the Genetic Algorithms (GA) (Goldberg,1989), Particle Swarm Optimization (PSO) (Kennedy and Eberhart, 1995), Quantum Particle Swarm Optimization (QPSO) (Sun and Xu, 2004), Differential Evolution (DE) (Storn and Price, 1995), Bacterial foraging (Passimo, 2002), Artificial Bees Colony (ABC) algorithm (Karaboga, 2005) and Firefly (FA) algorithm (Yang, 2008),Cuckoo Search (CS) (Yang and Deb, 2009) and BAT algorithm (Yang, 2010).