Edge Detection and Region Growing Image Segmentation Algorithms

Edge detection and region growing are two commonly used algorithms in image segmentation, each suitable for different scenarios. Edge detection primarily identifies object contours within images, while region growing progressively expands regions based on pixel similarity to achieve target extraction.

### Edge Detection Algorithm The core concept of edge detection involves identifying abrupt changes in pixel intensity values, typically using gradient operators (such as Sobel, Prewitt, and Canny) to calculate the rate of pixel variation. Canny edge detection is widely adopted due to its excellent noise resistance and precision. Its implementation steps include: Gaussian filtering for image smoothing, gradient magnitude and direction computation, non-maximum suppression to eliminate false edges, and double threshold detection to determine valid edges. In MATLAB implementation, the algorithm can be optimized by adjusting sigma values for Gaussian blurring and tuning hysteresis thresholds for better edge connectivity.

### Region Growing Algorithm Region growing is a segmentation method based on similarity criteria that starts from seed points and gradually merges neighboring pixels to form continuous regions. Key implementation steps include: Seed Selection: Manual or automatic selection of initial pixel points, where automated methods can utilize intensity peaks or threshold-based approaches. Growth Criterion: Typically uses grayscale, texture, or color similarity to determine adjacent pixel merging, with customizable similarity thresholds controlling region homogeneity. Termination Condition: Growth stops when no new pixels meet the merging criteria, implemented through iterative neighborhood scanning and queue-based pixel evaluation.

### MATLAB Implementation Guidelines In MATLAB, edge detection can be directly implemented using the `edge` function, which supports multiple operators (e.g., `'Sobel'`, `'Canny'`) with customizable parameters for sensitivity control. Region growing requires custom implementation through iterative loops and neighborhood comparisons, where functions like `imregionalmax` for local maxima detection or `graythresh` for automatic thresholding can assist in seed selection. The algorithm typically employs queue data structures for efficient pixel propagation and uses logical masks to track grown regions.

Both algorithms can be combined for enhanced segmentation accuracy—for example, using edge detection to initially locate object boundaries followed by region growing to fill interior areas. This hybrid approach compensates for edge detection's susceptibility to fragmentation and region growing's dependency on seed placement, achieving more robust segmentation results through complementary strengths.