FCM-Based Image Segmentation Method

The FCM-based image segmentation method delivers excellent performance, validated through multiple experiments, and has extensive applications in the field of image processing. By performing cluster analysis on images, this method partitions pixels into distinct regions through iterative optimization of a fuzzy membership function. Key implementation steps typically involve: 1) Initializing cluster centers and membership values, 2) Calculating Euclidean distance between pixels and centroids, 3) Updating membership grades using the fuzzifier parameter (usually set between 1.5-2.0), and 4) Recomputing cluster centers until convergence criteria are met. The algorithm demonstrates strong robustness and adaptability, capable of handling various image types and sizes through adjustable parameters like cluster number selection and termination tolerance. In practical applications, we recommend considering FCM-based segmentation when selecting image processing methods, as it provides superior results particularly for images with overlapping intensity distributions. Implementation can be enhanced using MATLAB's fcm function or Python's sklearn.extensions with appropriate preprocessing for optimal performance.