Shahrood University of Technology Journal of AI and Data Mining 2322-5211 14 2 2026 04 01 Conditional Spatial Gustafson-Kessel Clustering Algorithm Based on Information Theory for Segmenting Brain MRI Images 129 144 3707 10.22044/jadm.2025.16712.2798 EN Ali Fahmi Jafargholkhanloo Department of Engineering Sciences, Faculty of Advanced Technologies, University of Mohaghegh Ardabili, Namin, Iran. Mousa Shamsi Department of Bioelectric, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran. Mahdi Bashiri Bawil Department of Bioelectric, Faculty of Biomedical Engineering, Sahand University of Technology, Tabriz, Iran. Journal Article 2025 08 20 Magnetic Resonance Imaging (MRI) often suffers from noise and Intensity Non-Uniformity (INU), making segmentation a challenging task. The Fuzzy C-Means (FCM) algorithm, a widely used clustering method for image segmentation, is highly sensitive to noise and its convergence rate depends on data distribution. FCM employs the Euclidean distance metric, which fails to adapt to variations in data point distributions within compact and similarly shaped clusters. Additionally, this metric is not locally adaptive to different cluster shapes. This paper introduces a Conditional Spatial Gustafson-Kessel Clustering Algorithm based on Information Theory (CSGKIT) to address these challenges. First, information theory is incorporated to enhance the algorithm's robustness against noise and improve segmentation accuracy. Second, the Mahalanobis distance replaces the Euclidean distance to better accommodate cluster shapes during the clustering process. Finally, a conditional spatial approach uses a fuzzy-weighted membership matrix to incorporate local spatial interactions between neighboring pixels. The proposed CSGKIT algorithm is evaluated on two datasets: the BrainWeb simulated dataset and the Open Access Series of Imaging Studies (OASIS) dataset. Experimental results indicate that CSGKIT outperforms other FCM-based algorithms in segmentation accuracy across various tissue types. https://jad.shahroodut.ac.ir/article_3707_8d863e9f35bcfade3253b7c933f2adf4.pdf