@Article{cmc.2020.09519,
AUTHOR = {A. Renugambal, K. Selva Bhuvaneswari},
TITLE = {Image Segmentation of Brain MR Images Using Otsu’s Based Hybrid WCMFO Algorithm},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {64},
YEAR = {2020},
NUMBER = {2},
PAGES = {681--700},
URL = {http://www.techscience.com/cmc/v64n2/39325},
ISSN = {1546-2226},
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 T<sub>2</sub>-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.},
DOI = {10.32604/cmc.2020.09519}
}