MATLAB Implementation of Region Growing Algorithm

Region growing is a classical image segmentation method suitable for aggregating pixels with similar characteristics into continuous regions. When implementing in MATLAB, the key focus lies in flexibly controlling growth criteria and interactive seed point configuration.

Core Logic: Seed Point Selection: Allows users to specify growth starting points via mouse clicks or coordinate input, using the pixel value as initial reference. Growth Rules: Controls similarity judgment for adjacent pixels (such as grayscale difference or RGB distance) by adjusting threshold parameters, with qualifying pixels incorporated into the region. Neighborhood Expansion: Typically employs 4-connected or 8-connected neighborhood traversal, using queue or stack structures to manage pixels awaiting inspection, ensuring complete growth.

Parametric Design: Dynamic Threshold: Real-time adjustment of grayscale/color tolerance through sliders or input boxes to observe segmentation effects. Region Constraints: Optional limitation of maximum growth area or shape complexity to prevent over-segmentation. Multi-channel Support: For color images, individual channel weights can be set separately to adapt to different scenarios.

Implementation Techniques: Utilizes logical matrices to track visited pixels and avoid redundant calculations. Combines `imshow` with `drawpoint` interactive tools to enhance seed point annotation experience. Employs `ginput` function to capture user-click coordinates for dynamic seed setting.

The advantage of this approach lies in its intuitive controllability, making it suitable for scenarios requiring manual intervention such as medical imaging or target contour extraction.