MATLAB Implementation of Region Growing and Watershed Algorithms for Image Segmentation

Region growing and watershed algorithms are two commonly used methods in image segmentation, suitable for scenarios such as medical imaging and remote sensing. MATLAB provides a powerful image processing toolbox that can efficiently implement these algorithms. ### Region Growing Algorithm The core concept of region growing is to expand regions gradually from seed points based on pixel similarity criteria (such as gray value or texture). In MATLAB, this can be implemented using iterative or queue structures: Seed point selection: Manually or automatically select initial pixels as starting points for growth. Similarity determination: Compare adjacent pixels with the current region using gray difference or standard deviation thresholds, merging if criteria are met. Iterative expansion: Repeat the process until no new pixels satisfy the conditions. In MATLAB implementation, developers typically use while loops with neighbor checking (e.g., using imdiff or regionprops functions) and maintain a list of boundary pixels for efficient processing. This algorithm works well for images with clear structures but is sensitive to noise, requiring smooth preprocessing such as Gaussian filtering using imgaussfilt function. ### Watershed Algorithm The watershed algorithm treats images as topographic surfaces and divides regions by simulating a "flooding" process: Gradient calculation: First perform gradient operations on the image (e.g., using Sobel operator with imgradient function) to highlight edges. Marker extraction: Determine initial watershed positions through minima markers or prior knowledge using imextendedmin or imimposemin functions. Watershed line generation: Simulate water level rise based on gradient images, merging similar regions while preserving boundaries using the watershed function. MATLAB's built-in watershed function can be directly called, but users should be aware of over-segmentation issues. Typically, morphological processing (using imopen, imclose) or marker-controlled approaches are combined to optimize results. ### Extended Applications Medical imaging: Tumor segmentation, vessel extraction using specialized toolboxes like Image Processing Toolbox for DICOM images. Remote sensing: Land cover classification, edge detection with multispectral image processing capabilities. Industrial inspection: Defect region marking for quality control applications. Both methods can be combined, for example using region growing for coarse segmentation first, then applying watershed for boundary refinement. MATLAB's interactive tools like Image Segmenter App also support visual parameter adjustment with real-time preview capabilities.