A. Renugambal1, *, K. Selva Bhuvaneswari2
CMC-Computers, Materials & Continua, Vol.64, No.2, pp. 681-700, 2020, DOI:10.32604/cmc.2020.09519
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… More >
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