FCM: A Practical Algorithm for Medical Image Segmentation

In medical image segmentation, FCM (Fuzzy C-Means) is extensively applied and recognized as a practical algorithm. The implementation typically involves iterative optimization of cluster centroids and membership values using weighted similarity measures. This algorithm can effectively segment MRI human brain images, assisting physicians in better understanding and analyzing image data through probabilistic tissue classification. Furthermore, the FCM algorithm has numerous enhancements including kernel-based distance metrics and spatial constraint integration to improve its performance and accuracy. This program leverages an optimized FCM implementation with adaptive thresholding and spatial contextual information, providing more efficient and precise solutions for medical image segmentation tasks. The code structure includes modular functions for data preprocessing, cluster initialization, membership updating, and convergence validation.