Brain MR Image Multilevel Thresholding by Using Particle Swarm Optimization, Otsu Method and Anisotropic Diffusion

Abdul Kayom Md Khairuzzaman and Saurabh Chaudhury
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Abdul Kayom Md Khairuzzaman: Department of Electrical Engineering, National Institute of Technology Silchar, Silchar, India
Saurabh Chaudhury: Department of Electrical Engineering, National Institute of Technology Silchar, Silchar, India

International Journal of Applied Metaheuristic Computing (IJAMC), 2019, vol. 10, issue 3, 91-106

Abstract: Multilevel thresholding is widely used in brain magnetic resonance (MR) image segmentation. In this article, a multilevel thresholding-based brain MR image segmentation technique is proposed. The image is first filtered using anisotropic diffusion. Then multilevel thresholding based on particle swarm optimization (PSO) is performed on the filtered image to get the final segmented image. Otsu function is used to select the thresholds. The proposed technique is compared with standard PSO and bacterial foraging optimization (BFO) based multilevel thresholding techniques. The objective image quality metrics such as Peak Signal to Noise Ratio (PSNR) and Mean Structural SIMilarity (MSSIM) index are used to evaluate the quality of the segmented images. The experimental results suggest that the proposed technique gives significantly better-quality image segmentation compared to the other techniques when applied to T2-weitghted brain MR images.

Date: 2019
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