Image Thresholding Using Fuzzy C-Means Clustering Algorithm

This code performs image segmentation using fuzzy c-means thresholding, which produces better results than Otsu's method in many scenarios. The implementation is based on the fuzzy clustering algorithm that classifies pixels into different categories by measuring their membership degrees to multiple cluster centers. Unlike traditional Otsu's method that uses a single hard threshold, fuzzy c-means thresholding employs a probabilistic approach where each pixel's classification is determined by its distance to various cluster centroids. Key implementation aspects include: - Initialization of cluster centers using image histogram analysis - Iterative calculation of membership values using distance metrics - Continuous update of cluster centers based on weighted pixel values - Convergence checking using membership stability criteria This method demonstrates enhanced robustness and accuracy when processing images with uneven illumination, complex textures, or significant noise. The algorithm's fuzzy logic foundation allows it to handle ambiguous boundary regions more effectively than binary thresholding approaches. The code typically involves optimizing parameters such as the fuzziness exponent and termination tolerance to achieve optimal segmentation results for different image characteristics.