MATLAB Implementation of Region Growing Algorithm for Image Segmentation

Region growing algorithm is a pixel similarity-based image segmentation method that forms regions by progressively merging adjacent similar pixels starting from seed points. The MATLAB implementation demonstrates the core approach: ### Algorithm Workflow Interactive seed selection Users specify initial seed points by left-clicking on the image, supporting multiple point selection to cover complex regions Right-clicking terminates the seed selection phase Code implementation uses MATLAB's `ginput` function to capture mouse coordinates and convert them to image pixel positions Growth condition determination Pixel intensity difference from seed point less than preset threshold Extensible to multi-channel images using color space distance calculations Implementation typically uses absolute difference comparison with adjustable threshold values Region expansion mechanism Uses queue structure to manage neighborhood pixels for checking (4-neighborhood/8-neighborhood) Processes pixels from queue head, adding qualified pixels to the region and marking them MATLAB implementation commonly employs while-loops with queue management for efficient pixel processing ### Key Implementation Points Coordinate conversion from `ginput` output to image matrix indices Growth threshold adjustment based on image dynamic range, typically set at 10%-20% of grayscale value differences Logical matrix usage to track visited pixels and prevent redundant calculations Visualization with distinct colors highlighting grown regions versus original seed points using MATLAB's plotting functions ### Optimization Directions Adding preprocessing steps (like Gaussian filtering) to improve segmentation stability in noisy images Implementing dynamic threshold mechanisms allowing adaptive thresholds for different seed points Incorporating edge detection results as growth termination conditions Code optimization through maximum iteration limits to handle performance constraints with large images This implementation demonstrates the intuitive and interactive characteristics of region growing algorithms, making them suitable for segmentation scenarios requiring manual intervention like medical imaging. Algorithm efficiency depends on image size and region complexity, with performance optimizations achievable through iteration limits and efficient data structures.