A Novel Region Growing Algorithm with Automated Seed Selection

The novel region growing algorithm introduces innovations over traditional methods, primarily addressing the challenge of automated seed point selection to eliminate manual intervention during application. This algorithm analyzes pixel feature distributions within images to automatically identify optimal regions as growth starting points, thereby improving segmentation accuracy and efficiency.

Compared with conventional approaches, this algorithm offers several advantages: Automated Seed Selection: Leverages global or local statistical characteristics (such as gradient, texture, or grayscale distribution) to dynamically determine seed points, reducing the need for manual annotation. Implementation typically involves calculating feature maps and applying statistical filters to identify regions with homogeneous properties. Enhanced Noise Resistance: Incorporates regional consistency constraints to prevent erroneous growth caused by noise or weak boundaries. This can be implemented through connectivity checks and boundary strength evaluation using gradient-based metrics. Efficient Convergence: Optimizes growth rules through adaptive thresholds or probability models, ensuring rapid convergence even in complex scenarios. Code implementation often utilizes queue-based pixel propagation with dynamically updated inclusion criteria.

The algorithm is suitable for medical image analysis, remote sensing image segmentation, and other domains requiring high-precision automated processing. It particularly excels in scenarios demanding robust automatic handling. Future research directions may explore multimodal data fusion or integration with deep learning techniques for further improvements.