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This paper proposes a new image thresholding segmentation approach using theheuristic method, Convergent Heterogeneous Particle Swarm Optimizationalgorithm. The proposed algorithm incorporates a new strategy of searching theproblem space by dividing the swarm into subswarms. Each subswarm particlessearch for better solution separately lead to better exploitation while theycooperate with each other to find the best global position. The consequence ofthe aforementioned cooperation is better exploration, convergence and it ablethe algorithm to jump from local optimal solution to the better spots. Apractical application of this method is demonstrated for the problem of medicalimage thresholding segmentation. We considered two classical thresholdingtechniques of Otsu and Kapur separately as the objective function for theoptimization method and applied on a set of brain MR images. Comparativeexperimental results reveal that the proposed method outperforms another stateof the art method from the literature in terms of accuracy, computation timeand stable results.

Multilevel Thresholding Segmentation of T2 weighted Brain MRI images using Convergent Heterogeneous Particle Swarm Optimization