MATLAB Implementation of EM Segmentation Algorithm

Our MATLAB implementation of the Expectation-Maximization (EM) segmentation algorithm has yielded highly satisfactory results. This algorithm employs probabilistic modeling where each pixel is assigned to clusters through iterative expectation (E-step) and maximization (M-step) phases. The E-step calculates posterior probabilities using Gaussian mixture models, while the M-step updates cluster parameters (mean and covariance) through maximum likelihood estimation. The implementation includes histogram initialization and convergence checking using likelihood thresholding. Widely applied in image processing, this algorithm demonstrates remarkable performance across various domains. It effectively partitions images into distinct regions, enabling better understanding and analysis of image information. The implementation utilizes MATLAB's statistical toolbox for probability density calculations and includes custom functions for cluster centroid initialization and boundary refinement. This implementation holds significant importance for both image processing research and practical applications. In future studies, we plan to enhance the algorithm by incorporating spatial constraints through Markov Random Fields and optimizing computational efficiency using vectorized operations for larger datasets, aiming to achieve more precise and efficient segmentation outcomes.