Optimized Brain Tumor Segmentation Using Chimp Optimization Algorithm and Type-2 Intuitionistic Fuzzy C-Means Clustering

Medical image segmentation plays a vital role in diagnostic imaging, particularly for measuring brain tumor morphology in MRI scans, which directly influences treatment planning, prognosis, and radiological interpretation. However, traditional segmentation techniques often struggle with low contrast, bias-field distortion, and tumor regions that azimuthally encircle healthy tissue. To address these challenges, a Chimp Optimization Algorithm-based Type-2 Intuitionistic Fuzzy C-Means Clustering (COA-T2FCM) framework is proposed. This method integrates Type-2 Intuitionistic Fuzzy C-Means (T2IFCM) clustering with a novel oppositional perturbation mechanism that simultaneously optimizes partition centroids and the fuzzification exponent. The Chimp Optimization Algorithm (COA) efficiently explores the parameter hyperspace, enhancing convergence to the global minima. By employing intuitionistic set theory applied to MRI histograms, the approach adapts to electromagnetic interference, noise, and bias-field distortions, while capturing classificatory ambiguity and pixel classification hesitancy. These measures are embedded into MRI reformulated functional calculations to quantitatively account for intuitionistic noise. The COA-T2FCM framework was implemented in MATLAB and evaluated using publicly available datasets. Performance assessment employed global segmentation accuracy, true positive rate (sensitivity), and Dice similarity coefficient. Experimental results demonstrated a Dice coefficient of 0.95, accuracy of 96%, sensitivity of 98%, and specificity of 97%. Comparative analysis against classical fuzzy c-means (FCM) and centroid-based k-means clustering revealed that the proposed method consistently outperformed these conventional approaches across all metrics. The findings confirm that COA-T2FCM delivers superior segmentation accuracy, robustness, and adaptability for brain tumor MRI analysis, making it a promising tool for clinical applications requiring precise tumor delineation under challenging imaging conditions.

Keywords:
Brain Tumor Segmentation; Chimp Optimization Algorithm; Type-2 Intuitionistic Fuzzy C-Means; MRI (Magnetic Resonance Imaging) Image Analysis; Medical Image Processing.

COA-T2FCM: Hybrid Chimp Optimization with Type-2 Fuzzy C-Means for MRI Segmentation.

Optimized Clusters and Fuzzifier Parameters Improve MRI Brain Tumor Accuracy.

Proposed COA-T2FCM Outperforms Existing Methods in Robust MRI Tumor Segmentation.