FCM Medical Image Segmentation Algorithm

The FCM (Fuzzy C-Means) medical image segmentation algorithm is specifically designed to handle various irregularly shaped medical images. It employs fuzzy clustering principles that assign pixels to multiple clusters with varying degrees of membership, making it particularly effective for ambiguous boundary regions common in medical imaging.

The algorithm has undergone comprehensive debugging and optimization, ensuring stable and reliable performance across different scenarios. Key implementation features include adaptive centroid initialization, membership matrix optimization, and convergence criteria tuning. The code structure supports flexible parameter configuration for different medical imaging modalities (CT, MRI, ultrasound).

This implementation has demonstrated excellent results in practical applications, significantly improving segmentation accuracy and efficiency. The algorithm's robust performance can substantially enhance medical research capabilities and clinical practice, offering new possibilities for automated diagnosis and treatment planning. The modular code design allows easy integration with existing medical imaging pipelines and supports further customization for specific use cases.