Mohamed Abdel-Basset1, Reda Mohamed1, Mohamed Abouhawwash2,3, Ripon K. Chakrabortty4, Michael J. Ryan4, Yunyoung Nam5,*
CMC-Computers, Materials & Continua, Vol.68, No.3, pp. 2961-2977, 2021, DOI:10.32604/cmc.2021.016956
Abstract Image segmentation is vital when analyzing medical images, especially magnetic resonance (MR) images of the brain. Recently, several image segmentation techniques based on multilevel thresholding have been proposed for medical image segmentation; however, the algorithms become trapped in local minima and have low convergence speeds, particularly as the number of threshold levels increases. Consequently, in this paper, we develop a new multilevel thresholding image segmentation technique based on the jellyfish search algorithm (JSA) (an optimizer). We modify the JSA to prevent descents into local minima, and we accelerate convergence toward optimal solutions. The improvement is achieved by applying two novel… More >
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