K-Means-Based Color Image Segmentation Algorithm Implementation in MATLAB

In this project, I implemented a color image segmentation algorithm using K-means clustering methodology in MATLAB. The algorithm was programmed using fundamental MATLAB syntax and basic programming constructs to ensure clarity and accessibility for users. Key implementation aspects include RGB-to-L*a*b* color space conversion for better clustering performance, automated centroid initialization using k-means++ technique, and iterative distance calculation using Euclidean metric for pixel-cluster assignment. The algorithm features cluster reassignment with mean recalculation and convergence checking based on centroid stability. This implementation can be directly applied to biological image segmentation tasks, particularly for color cell images, demonstrating reasonably accurate segmentation results. Additional optimizations were incorporated to enhance computational efficiency and segmentation precision, including vectorized operations for faster processing and cluster validation using silhouette analysis. Overall, this algorithm serves as a practical tool for various image processing applications, providing customizable parameters for cluster count specification and maximum iteration controls.