MATLAB Implementation of Region Growing Algorithm for Image Segmentation

Region growing algorithm is a pixel-similarity-based image segmentation technique that extracts target regions by selecting seed points and progressively merging adjacent similar pixels. Implementing this algorithm in MATLAB typically involves handling several critical steps: Seed Point Selection The algorithm requires selecting one or more starting points (seed points), usually specified manually or automatically chosen through specific conditions. The choice of seed points directly impacts the final segmentation results. In MATLAB implementation, seed selection can be programmed using functions like ginput for interactive selection or find for automatic detection based on intensity thresholds. Similarity Criterion The core of region growing lies in defining pixel similarity, with common standards including grayscale difference, color distance, or texture features. For example, if the grayscale difference between adjacent pixels and the seed point is below a threshold, the pixel is incorporated into the target region. MATLAB implementation typically uses absolute difference calculations with threshold comparisons, such as abs(current_pixel - seed_value) < threshold. Neighborhood Expansion Starting from seed points, the algorithm examines pixels in their 8-neighborhood or 4-neighborhood and decides whether to merge them based on similarity criteria. Newly merged pixels become starting points for subsequent growth, resembling a diffusion process similar to breadth-first search. In MATLAB, this can be efficiently implemented using queue data structures and neighbor coordinate calculations with offsets like [-1,0; 1,0; 0,-1; 0,1] for 4-connectivity. Termination Conditions Growth stops when no more qualifying pixels can be added. Additional constraints like maximum/minimum area limits can be set to prevent over-segmentation or under-segmentation. MATLAB implementations often use while loops with exit conditions based on queue emptiness and area size checks using regionprops or custom counter variables. During MATLAB implementation, attention should be paid to optimizing matrix operations to avoid performance degradation from loops. For instance, using queues to manage pixels awaiting inspection and logical matrices to record visited positions can significantly improve efficiency. The algorithm can utilize MATLAB's queue functions or implement custom FIFO structures using arrays. Extension Approaches: Combine with edge detection results to adjust growth thresholds and improve boundary accuracy Implement multi-seed point parallel growth followed by region merging strategies to handle overlapping areas For color images, convert to HSV or LAB color spaces to define more robust similarity metrics using multidimensional distance calculations This algorithm is suitable for extracting homogeneous targets in medical images or remote sensing imagery, but it's sensitive to noise and initial seed positions. Practical applications often require integration with other filtering methods, such as pre-processing with medfilt2 or wiener2 for noise reduction.