Image Segmentation Using C-Means Clustering Method

This implementation demonstrates image segmentation using the C-means clustering method. The MATLAB-based approach follows these key steps: 1. Data Preprocessing: Convert the input image to grayscale using MATLAB's rgb2gray() function to simplify processing and reduce computational complexity. This step ensures uniform pixel intensity representation for clustering. 2. Cluster Center Initialization: Initialize K cluster centers based on the image characteristics. The implementation typically uses kmeans initialization methods or random selection from pixel intensity values, ensuring proper spread across the intensity range. 3. Iterative Cluster Center Update: Calculate the Euclidean distance between each pixel and cluster centers using vectorized operations for efficiency. Assign pixels to the nearest cluster and recalculate centroids by averaging member pixel values. This iterative process employs MATLAB's matrix operations for optimal performance. 4. Convergence Check: Monitor centroid movement between iterations. The algorithm terminates when centroid positions stabilize below a predefined threshold (e.g., 1e-5), indicating convergence. This is implemented through a while-loop with convergence criteria checking. 5. Image Segmentation: Apply the final cluster centers to segment the image by replacing pixel values with their respective cluster centroids. This creates distinct regions corresponding to different clusters, visualized using MATLAB's image display functions. 6. Result Analysis: Evaluate segmentation quality using metrics like within-cluster sum of squares or silhouette coefficients. The experimental report documents the complete workflow, parameter settings, computational efficiency, and segmentation outcomes with visual comparisons. The MATLAB implementation leverages built-in functions for image processing and optimized clustering algorithms, while the experimental report provides detailed code explanations, performance analysis, and methodological insights. The code structure includes modular functions for each step, allowing easy parameter adjustment and algorithm extension.