Optimal multilevel thresholding using molecular kinetic theory optimization algorithm

Multilevel thresholding is one of the most widely used techniques for image segmentation. However, the conventional multilevel thresholding methods are time-consuming algorithms since they exhaustively search for the optimal thresholds to optimize the objective functions. In this paper, a molecular kinetic theory optimization algorithm (MKTOA) is applied to overcome this drawback. MKTOA is used to find the optimal threshold values for maximizing the Kapur’s and Otsu’s objective functions. Three different methods are compared to this proposed method: the molecular force based particle swarm optimization (MPSO) algorithm, the differential evolution (DE) algorithm and the bacterial foraging (BF) algorithm. Experimental results show that MKTOA is much better in terms of robustness, computational efficiency, peak signal to noise ratio (PSNR) and ability to conquer “the Curse of Dimensionality” than MPSO, DE and BF.