FCM Image Segmentation - Fuzzy C-Means Algorithm Implementation

This article discusses FCM (Fuzzy C-Means) image segmentation technology, a fundamental method in image processing. This technique partitions images into distinct regions or objects to facilitate further analysis and processing. FCM image segmentation employs a fuzzy clustering algorithm based on principles from fuzzy logic and cluster analysis. By grouping image pixels into clusters through an iterative optimization process, FCM segmentation extracts various features and structures from images, providing a foundation for subsequent image processing tasks. The algorithm typically involves initializing cluster centers, calculating membership degrees for each pixel belonging to different clusters using distance metrics, and iteratively updating cluster centers until convergence. Key implementation functions often include computing fuzzy membership matrices and Euclidean distance measurements. Due to its effectiveness in handling uncertainty and overlapping regions, FCM image segmentation has gained widespread application and research interest in computer vision and image processing domains, particularly in medical imaging and pattern recognition.