Brain Tissue Segmentation Program with FCM Algorithm

I have developed a specialized program for brain tissue segmentation that implements the Fuzzy C-Means (FCM) clustering algorithm for automated tissue classification. The program features a complete MATLAB implementation with pre-configured parameters including cluster centroids initialization, membership function calculation, and iterative optimization until convergence criteria are met. This user-friendly solution requires no additional configuration - users can execute the main script directly to obtain segmentation results. The algorithm processes neuroimaging data through multiple stages: pre-processing for intensity normalization, FCM clustering for voxel classification based on fuzzy membership values, and post-processing for morphological refinement. For researchers, this tool provides significant advantages in analyzing brain tissue architecture and functional correlations. The program delivers rapid and accurate segmentation outcomes, enabling efficient investigation of neurological disorder mechanisms. In clinical applications, it supports medical professionals in diagnostic imaging and treatment planning for cerebral pathologies. Key technical features include: - Automated cluster number determination using validity indices - Adaptive distance metrics for improved tissue boundary detection - Batch processing capability for multiple dataset handling - Results export in DICOM and NIfTI formats for compatibility with clinical workflows This robust implementation serves as a valuable resource in computational neuroscience and medical imaging, facilitating advancements in both brain research and clinical practice through reliable tissue segmentation capabilities.