Dominant Gray Level-based Genetic K-means Clustering Algorithm for MRI Image Segmentation
Received: 22 February 2022 | Revised: 28 March 2024 | Accepted: 6 April 2024 | Online: 1 June 2024
Corresponding author: Maha Ibrahim Khaleel
Abstract
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.
Keywords:
image processing, image segmention , k-means algorithm, clustering, genetic algorithm, MRIDownloads
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Copyright (c) 2024 Maha Ibrahim Khaleel, Musab Ahmed Mohammed, Maryam Qays
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