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

TL;DR

This paper introduces a novel multilevel thresholding segmentation method for brain MRI images using a convergent heterogeneous particle swarm optimization algorithm, improving accuracy and efficiency over existing techniques.

Contribution

It presents a new hybrid swarm optimization algorithm with subswarm cooperation for improved medical image segmentation performance.

Findings

Outperforms existing methods in accuracy

Reduces computation time

Provides stable segmentation results

Abstract

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.