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Multilevel Thresholding Segmentation of T2 weighted Brain MRI images using Convergent Heterogeneous Particle Swarm Optimization
This paper proposes a new image thresholding segmentation approach using the
heuristic method, Convergent Heterogeneous Particle Swarm Optimization
algorithm. The proposed algorithm incorporates a new strategy of searching the
problem space by dividing the swarm into subswarms. Each subswarm particles
search for better solution separately lead to better exploitation while they
cooperate with each other to find the best global position. The consequence of
the aforementioned cooperation is better exploration, convergence and it able
the algorithm to jump from local optimal solution to the better spots. A
practical application of this method is demonstrated for the problem of medical
image thresholding segmentation. We considered two classical thresholding
techniques of Otsu and Kapur separately as the objective function for the
optimization method and applied on a set of brain MR images. Comparative
experimental results reveal that the proposed method outperforms another state
of the art method from the literature in terms of accuracy, computation time
and stable results.Comment: Journa