Multilevel Thresholding Segmentation of T2 weighted Brain MRI images using Convergent Heterogeneous Particle Swarm Optimization
This is an incremental improvement for medical image analysis, specifically targeting brain MRI segmentation to enhance diagnostic accuracy and efficiency.
The paper tackles the problem of multilevel thresholding segmentation for T2-weighted brain MRI images by proposing a Convergent Heterogeneous Particle Swarm Optimization algorithm, which outperforms a state-of-the-art method with improved accuracy, faster computation time, and more stable results.
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.