Kapur's Entropy based Hybridised WCMFO Algorithm for Brain MR Image Segmentation

This article proposes a Kapur-based hybridized Water Cycle and Moth-Flame Optimisation (WCMFO) algorithm that combines a water cycle algorithm (WCA) and moth flame optimisation (MFO) in multilevel thresholding of brain MR image segmentation. The WCMFO algorithm, proposed by Khalilpourazari and Khalilpourazary, gives both WCA and MFO advantages, while avoiding some of the drawbacks of either approach on its own, as demonstrated by faster convergence with broader exploration and exploitation capabilities. Experiments on 10 axial, T-2-weighted test images were performed using Kapur entropy as the objective function at a threshold level of m = 2-5. The spiral movement of the behaviour of the moths is used for better exploitation in the WCA to find the global optimum values. WCMFO results, such as objective function value, peak signal to noise ratio, Central processing unit time and standard deviation, are collated and compared with other existing adaptive wind-driven optimization algorithm, adaptive bacterial foraging and particle swarm optimization algorithms. Experimental findings and comparison demonstrated that hybridized WCMFO algorithm was superior to the other algorithms. Moreover, the best segmentation is achieved on grey matter, white matter and cerebrospinal fluid that allows for better clinical decision-making and diagnosis in the medical field. Therefore, the proposed multilevel thresholding-based hybridized WCMFO algorithm is believed to be the most prominent preference for segmenting such complex brain images.

Automated summary

This article proposes a Kapur-based hybridized algorithm that combines a water cycle algorithm and moth flame optimisation in brain MR image segmentation. The algorithm shows faster convergence and broader exploration and exploitation capabilities compared to other adaptive optimization algorithms.