Development of a New Optimal Multilevel Thresholding Using Improved Particle Swarm Optimization Algorithm for Image Segmentation

Image thresholding is a very common image processing operation, since all image processing schemes need some sort of operation of the pixels into different classes. In order to determine thresholds, most methods analyze the histogram of the image. The optimal thresholds are often found by either minimizing or maximizing an objective function with respect to the values of the thresholds. In this paper, improved particle swarm optimization (IPSO) based multilevel thresholding has been proposed for the minimization of objective function. The chaotic sequences are included in the inertia weight factor of the classical PSO to improve the searching capability of the algorithm. The experimental results show that the proposed method can make optimal thresholding applicable in case of multilevel thresholding and the performances are better than those of some property–based multilevel thresholding methods.

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