MATLAB Region Growing Algorithm for Image Segmentation

Region growing algorithm is a classical image segmentation method particularly suitable for processing images with homogeneous regions. MATLAB provides a flexible programming environment that makes implementing this algorithm highly intuitive and efficient.

Algorithm Basic Concept Region growing starts from user-specified seed points and progressively merges adjacent pixels with similar properties into the target region. The core mechanism involves comparing statistical characteristics (such as grayscale values, color, or texture) between current pixels and the seed points or growing regions. If the difference falls within a predefined threshold, the pixel is incorporated into the region.

Implementation Steps Define comparison range (threshold): Users need to specify an allowable pixel difference threshold, for example ±10 in grayscale values, which can be implemented using absolute difference calculations in MATLAB. Set number of seed points: Determine the number of initial seeds to be manually selected, typically corresponding to the number of target regions in the image. Manual seed point selection: Utilize MATLAB's graphical interface tools like `ginput` function to interactively select seed positions. The algorithm will initiate growth from these core points. Region expansion: Employ neighborhood search methods (4-connected or 8-connected) to continuously merge qualified pixels until no new pixels meet the inclusion criteria, typically implemented using queue or stack data structures for efficient pixel processing.

Advantages and Application Scenarios Semi-automated: Combines manual seed selection with automatic region expansion, balancing precision and efficiency. Adaptability: Particularly effective for targets with uniform grayscale, such as organs in medical imaging. Flexibility: Segmentation granularity can be controlled by adjusting thresholds or seed point locations through parameter tuning.

Important Considerations Seed point placement and quantity directly impact segmentation results; avoid positioning seeds near region boundaries to prevent inaccurate growth. Images with significant noise may require preprocessing (such as filtering using `imfilter` or `medfilt2` functions) to enhance algorithm robustness.

With proper parameter configuration, MATLAB's region growing algorithm can efficiently handle complex image segmentation tasks while providing opportunities for customization through additional coding enhancements.