Image Segmentation using FCM and Level Set Methods with Brain CT Case Study

This article explores image segmentation methodologies integrating Fuzzy C-Means (FCM) clustering with Level Set algorithms, using brain cranial images as a primary case study. The segmentation approach partitions images into distinct regions where each segment represents specific anatomical structures or pathological features. The implementation typically involves initial FCM clustering for pixel classification (using membership functions and cluster centroids optimization) followed by Level Set evolution for precise boundary refinement (through partial differential equations governing curve propagation). This hybrid technique achieves superior segmentation accuracy by leveraging FCM's robustness to intensity variations and Level Set's topological flexibility. In medical imaging applications, this method enables precise delineation of cerebral structures, assisting clinicians in diagnostic analysis and pathological assessment. The segmentation results facilitate enhanced understanding of brain functionality and disease manifestations, establishing FCM-Level Set integration as a significant methodology in medical image processing pipelines. Code implementation would typically involve MATLAB's image processing toolbox for FCM initialization coupled with variational calculus for Level Set evolution.