Open Access

ARTICLE

Image Segmentation of Brain MR Images Using Otsu’s Based Hybrid WCMFO Algorithm

A. Renugambal^{1, *}, K. Selva Bhuvaneswari²

1 Department of Mathematics, University College of Engineering Kancheepuram, Kanchipuram, 631552, India.
2 Department of Computer Science and Engineering, University College of Engineering Kancheepuram, Kanchipuram, 631552, India

* Corresponding Author: A. Renugambal. Email: .

Computers, Materials & Continua 2020, 64(2), 681-700. https://doi.org/10.32604/cmc.2020.09519

Received 25 December 2019; Accepted 07 April 2020; Issue published 10 June 2020

Abstract

In this study, a novel hybrid Water Cycle Moth-Flame Optimization (WCMFO) algorithm is proposed for multilevel thresholding brain image segmentation in Magnetic Resonance (MR) image slices. WCMFO constitutes a hybrid between the two techniques, comprising the water cycle and moth-flame optimization algorithms. The optimal thresholds are obtained by maximizing the between class variance (Otsu’s function) of the image. To test the performance of threshold searching process, the proposed algorithm has been evaluated on standard benchmark of ten axial T₂-weighted brain MR images for image segmentation. The experimental outcomes infer that it produces better optimal threshold values at a greater and quicker convergence rate. In contrast to other state-of-the-art methods, namely Adaptive Wind Driven Optimization (AWDO), Adaptive Bacterial Foraging (ABF) and Particle Swarm Optimization (PSO), the proposed algorithm has been found to be better at producing the best objective function, Peak Signal-to-Noise Ratio (PSNR), Standard Deviation (STD) and lower computational time values. Further, it was observed thatthe segmented image gives greater detail when the threshold level increases. Moreover, the statistical test result confirms that the best and mean values are almost zero and the average difference between best and mean value 1.86 is obtained through the 30 executions of the proposed algorithm.Thus, these images will lead to better segments of gray, white and cerebrospinal fluid that enable better clinical choices and diagnoses using a proposed algorithm.

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