Brain Tissue Segmentation from Magnetic Resonance Brain Images Using Histogram Based Swarm Optimization Techniques

Abstract

Background and Objective: In order to reduce time complexity and to improve the computational efficiency in diagnosing process, automated brain tissue segmentation for magnetic resonance brain images is proposed in this paper. Methods: This method incorporates two processes, the first one is preprocessing and the second one is segmentation of brain tissue using Histogram based Swarm Optimization techniques. The proposed method was investigated with images obtained from twenty volumes and eighteen volumes of T1-Weighted images obtained from Internet Brain Segmentation Repository (IBSR), Alzheimer disease images from Minimum Interval Resonance Imaging in Alzheimer's Disease (MIRIAD) and T2-Weighted real-time images collected from SBC Scan Center Dindigul. Results: The proposed technique was tested with three brain image datasets. Quantitative evaluation was done with Jaccard (JC) and Dice (DC) and also it was compared with existing swarm optimization techniques and other methods like Adaptive Maximum a posteriori probability (AMAP), Biased Maximum a posteriori Probability (BMAP), Maximum a posteriori Probability (MAP), Maximum Likelihood (ML) and Tree structure K-Means (TK-Means). Conclusion: The performance comparative analysis shows that our proposed method Histogram based Darwinian Particle Swarm Optimization (HDPSO) gives better results than other proposed techniques such as Histogram based Particle Swarm Optimization (HPSO), Histogram based Fractional Order Darwinian Particle Swarm Optimization (HFODPSO) and with existing swarm optimization techniques and other techniques like Adaptive Maximum a posteriori Probability (AMAP), Biased Maximum a posteriori Probability (BMAP), Maximum a posteriori Probability (MAP), Maximum Likelihood (ML) and Tree structure K-Means (TK-Means).