MATLAB Implementation of FCM Algorithm for Image Segmentation

Fuzzy C-Means (FCM) clustering algorithm, implemented in MATLAB, serves as an efficient open-source solution for image segmentation tasks. The source code is specifically optimized for this application, delivering exceptional computational speed. The implementation utilizes MATLAB's vectorization capabilities and optimized clustering functions to handle large-scale image data efficiently. Key functions include fcm() for cluster center initialization and membership calculation, along with distance metric optimization for faster convergence.

This FCM implementation finds widespread applications across various domains including computer vision, medical image processing, and autonomous driving systems. The algorithm works by iteratively updating cluster centers and membership values until optimal segmentation is achieved. Its efficiency and accuracy make it a preferred tool in the image segmentation field, capable of rapidly processing extensive image datasets while providing precise segmentation results. The code includes parameters for controlling cluster numbers and convergence thresholds, enabling users to balance between processing speed and segmentation accuracy according to their specific requirements.

The implementation features memory-efficient data handling through MATLAB's matrix operations and includes visualization functions to display segmentation results. Users can modify the fcm_options structure to adjust parameters like maximum iterations, objective function tolerance, and exponent for the partition matrix, allowing customization for different image types and segmentation needs.