Image Segmentation Using Region Growing Algorithm

Image segmentation using region growing is a fundamental computer vision technique that partitions digital images into contiguous regions with similar characteristics. The algorithm initiates from seed pixels and progressively incorporates adjacent pixels into the expanding region based on predefined growth criteria. This method finds extensive applications in medical image processing, object detection, and image analysis tasks.

Key implementation aspects include: The algorithm typically starts by selecting initial seed points manually or through automated methods like intensity thresholding. A common approach involves using a queue-based system where neighboring pixels are evaluated against similarity measures (e.g., intensity difference, texture features). The growth process continues iteratively until no more pixels satisfy the inclusion criteria, often implemented using connectivity rules (4-connected or 8-connected neighborhoods). Critical functions in programming implementations include region similarity calculation, boundary management, and termination condition checks.

Optimization techniques may involve multi-seed initialization for handling disconnected regions and dynamic threshold adjustment for adaptive growth control. The algorithm's performance can be enhanced through preprocessing steps like Gaussian smoothing to reduce noise sensitivity.