Region Growing Algorithm with Seed Point Initialization

Region growing is a pixel-similarity-based image segmentation algorithm that starts from predefined seed points and progressively merges adjacent pixels satisfying similarity criteria into the same region. This method is particularly effective for extracting objects with uniform texture or color consistency.

### Algorithm Implementation Approach Seed Point Selection: Manually or automatically designate one or multiple seed points as growth starting positions, typically located inside target regions. Similarity Criterion: Define pixel similarity metrics (e.g., grayscale difference, color distance) where only neighboring pixels with differences below a threshold are incorporated. Growth Process: 1. Initialize a queue with seed points 2. While queue is not empty: - Dequeue current pixel - Check its neighbors (4-connectivity or 8-connectivity) - If unvisited neighbors meet similarity threshold, add to region and enqueue Termination Condition: Growth completes when queue empties, marking all connected similar pixels as one region.

### Key Optimization Techniques Dynamic Thresholding: Adapt thresholds using local statistics to prevent over/under-segmentation Multi-Seed Fusion: Merge growth results from multiple seeds belonging to the same object Post-Processing: Apply morphological operations (opening/closing) to eliminate isolated noise points

The algorithm's strengths lie in intuitive logic and straightforward implementation, though it requires careful seed placement and threshold tuning, making it ideal for interactive segmentation scenarios. Code implementation typically involves queue management, neighbor traversal, and similarity validation functions.