Dominant Gray Level-based Genetic K-means Clustering Algorithm for MRI Image Segmentation


  • Maha Ibrahim Khaleel Department of Computer Technologies Engineering, Alsafwa University College, Iraq
  • Musab Ahmed Mohammed Department of English, University of Kirkuk, Iraq
  • Maryam Qays Department of Clinical Laboratory Sciences, Alzahraa University for Women, Iraq
Volume: 14 | Issue: 3 | Pages: 14355-14360 | June 2024 |


In this paper, a method and fresh results associated with medical image segmentation of brain Magnetic Resonance Imaging (MRI) scans are presented. Gray-converted segmentation and Genetic Algorithm (GA) are utilized along with unsupervised k-means classification. The image segmentation employed indicates the tissue type or the anatomical structure of each pixel. The cluster centroid initialization is performed by GA. GA offers efficient search processes (selection, crossover, and mutation), suited to determine global optima regarding centroid problems. As a result, this research offers more accurate, reliable, and efficient image segmentation for MRI, by improving the k-means algorithm with GA. The results indicate that the accuracy obtained from the proposed method is at least 3.5% higher than the PSO algorithm in this matter.


image processing, image segmention , k-means algorithm, clustering, genetic algorithm, MRI


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How to Cite

M. I. Khaleel, M. A. Mohammed, and M. Qays, “Dominant Gray Level-based Genetic K-means Clustering Algorithm for MRI Image Segmentation”, Eng. Technol. Appl. Sci. Res., vol. 14, no. 3, pp. 14355–14360, Jun. 2024.


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