Segmentation of WM, GM, and CSF from the Brain MRIs Using an Advanced Metaheuristic Approach

Abstract

Segmentation of brain tissues from the Magnetic Resonance Image (MRI) is vital to detect and diagnose brain-related diseases. Precise brain MRI segmentation is challenging because of the unpredictable anatomical structure of the brain, existence of Intensity Inhomogeneity (IIH), Partial Volume (PV) effects, and noise. Most of the brain disorders can be identified exactly by the segmentation of brain tissues, such as White Matter (WM), Gray Matter (GM), and Cerebrospinal Fluid (CSF). The optimization algorithmic approach is widely used for the brain image segmentation. In this work, a robust metaheuristic Multi-level Thresholding (MT) technique named as Advanced Wind Driven Optimization (AWDO) is employed for the brain MRI segmentation. The optimal MT values can be attained by combining the AWDO with the very popular Otsu’s objective function. The results obtained by the proposed AWDO in segmenting various brain images at an optimized value of threshold are compared with the groundtruths and have given the high values for the metrics such as sensitivity, specificity, segmentation accuracy, dice coefficient, and Precision. The values of the objective function, Peak Signal to Noise Ratio (PSNR), Standard Deviation (SD), and Convergence time of the proposed method are also being obtained at various threshold levels. The results are compared with the results of some existing algorithms like Multiplicative Intrinsic Component Optimization (MICO), Electromagnetism Optimization (EMO), Histogram based Darwinian Particle Swarm Optimization (HDPSO), Fully Convolutional Neural Networks (FCNN), and Advance Ant Colony Optimization (AACO). The results justified the notable performance of the proposed approach in segmenting different brain tissues.