K-Mean Based Hyper-Metaheuristic Grey Wolf and Cuckoo Search Optimizers for Automatic MRI Medical Image Clustering

Waleed A. Mahmoud  Al-Jawher; Shaimaa A.  Shaaban

doi:10.36371/port.2024.special.11

Authors

Waleed A. Mahmoud Al-Jawher Uruk University, Baghdad, Iraq.
Shaimaa A. Shaaban Uruk University, Baghdad, Iraq.

Abstract References Downloads Cite Statistics Share Information

In this paper a new clustering algorithm is proposed for optimal clustering of MRI medical image. In our proposed algorithm, the clustering process implemented by K-means clustering algorithm, due to its simplicity and speed. The optimization process was done by a well-known metaheuristic algorithms Grey Wolf Optimizer (GWO) and Cuckoo Search Optimizer. GWO is a metaheuristic algorithm inspired by the social hierarchy and hunting behavior of grey wolves. It mimics the leadership hierarchy and hunting strategies of wolves to explore the search space efficiently. GWO has shown promising performance in finding high-quality solutions compared to other well-established optimizers. It explores the solution space to find better cluster assignments that minimize the overall intra-cluster variance. By leveraging the exploration potential of GWO, the proposed algorithm aims to improve the quality of the clustering results. Furthermore, the Cuckoo Search Optimizer (CS) is combined with GWO to enhance the algorithm's ability to find a global solution. Cuckoo Search is a metaheuristic algorithm inspired by the breeding behavior of cuckoo birds. It employs random search and Levy flights to diversify the search process and avoid getting trapped in local optima. By combining CS with GWO, the proposed algorithm aims to increase the likelihood of finding the optimal clustering solution.

Keywords:

Clustering algorithm, Grey Wolf Optimizer (GWO), MRI medical image, Cuckoo Search Optimizer (CS)

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Al-Jawher, W. A. M. ., & Shaaban, S. A. . (2024). K-Mean Based Hyper-Metaheuristic Grey Wolf and Cuckoo Search Optimizers for Automatic MRI Medical Image Clustering . Journal Port Science Research, 7(issue), 109–120. https://doi.org/10.36371/port.2024.special.11