Ant Colony Optimization (ACO) Based Fuzzy C-Means (FCM) Clustering Approach for MRI Images Segmentation


  • Mohammed H. Al Aqad Department of Social Sciences at Management and Science University, Malaysia
  • Luis Miguel Oliveira de Barros Cardoso Department of Languages and Communication Sciences, Polytechnic Institute of Portalegre, Portugal



Thousands of real-life ants have been used to improve the Ant Colony Optimization (ACO) technique. Significant improvement can be noticed in segmented images using ACO-based rather than random initialization. The segmentation quality has improved as a result of the noise reduction. Clustering based on FCM is used to segment medical images. To avoid local optimal results, cluster centres are initially determined using ACO. This paper shows that our approach (ACO-FCM) provides significant improvements. In ACO-FCM, brain tissues are classified more accurately because there are more correctly classified pixels. As a result of our approach, such tissues can be classified more accurately based on spatial information


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How to Cite

M. H. Al Aqad and Luis Miguel Oliveira de Barros Cardoso, “Ant Colony Optimization (ACO) Based Fuzzy C-Means (FCM) Clustering Approach for MRI Images Segmentation”, WJCMS, vol. 2, no. 4, pp. 115–125, Dec. 2023, doi: 10.31185/wjcms.230.