An Image Segmentation Algorithm: Region Growing Approach and Implementation

Image segmentation represents a fundamental technique in computer vision, with region growing algorithms gaining widespread adoption due to their intuitiveness and effectiveness. This algorithm progressively merges similar pixels to form closed regions, ultimately producing segmentation results with well-defined boundaries.

The core principle of region growing initiates from pre-selected seed points, systematically examining neighboring pixels against specific criteria such as grayscale values, color similarity, or texture characteristics. When a pixel meets the established conditions, it becomes incorporated into the current region, with the process continuing outward until no further qualifying pixels can be found. From an implementation perspective, this typically involves queue-based pixel propagation using data structures like FIFO queues or priority queues for efficient neighborhood exploration.

The algorithm's primary advantage lies in generating regions with continuous boundary contours, effectively avoiding fragmentation issues common in edge detection methods. Since region growing operates on local similarity metrics, it demonstrates superior performance for targets with complex textures but strong internal consistency, such as organ tissues in medical imaging applications. Code implementations often utilize connectivity checks (4-connected or 8-connected neighborhoods) and similarity thresholding functions to maintain region homogeneity.

Critical implementation considerations include seed point selection strategies and growth rule configuration, which directly influence segmentation outcomes. Appropriate threshold settings help balance over-segmentation and under-segmentation problems, while improved algorithms incorporating dynamic threshold adaptation can enhance robustness across varying illumination conditions. Developers typically implement threshold calibration methods and multi-seedpoint initialization routines to handle complex real-world scenarios.