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

Author(s):

T. Priya and P. Kalavathi*   Pages 1 - 14 ( 14 )

Abstract:


Background and Objective: In order to reduce time complexity and to improve the computational efficiency in diagnosing process, an automated brain tissue segmentation for Magnetic Resonance brain images are proposed in this paper.

Methods: This method incorporated by two processes, first one is preprocessing and second one is segmentation of brain tissue using Histogram based Swarm Optimization techniques. 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.

Results: The proposed technique was tested with three brain image datasets. Quantitative evaluation was done with Jaccard (JC) and Dice (DC) and also it is 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 Likehood (ML) and Tree structure K-Means (TK-Means).

Conclusions: 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 Likehood (ML) and Tree structure K-Means (TK-Means).

Keywords:

Alzheimer disease, brain tissue segmentation, Darwinian particle swarm optimization, particle swarm optimization, fractional order Darwinian particle swarm optimization, histogram based segmentation

Affiliation:

Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Dindigul - 624302, Department of Computer Science and Applications, The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Dindigul - 624302



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