Brain Image Segmentation Using Region Growing Algorithm with Dual Seed Points

This article describes an image segmentation technique called the "Region Growing Algorithm" applied to brain image analysis. The algorithm utilizes two seed points to solve the challenge of complete white matter segmentation. This automated segmentation approach enhances the understanding of human brain structures and plays a vital role in medical image diagnostics. Key implementation aspects include: - Seed point initialization and neighbor pixel connectivity checks - Intensity-based similarity criteria for region expansion - Termination conditions when no more pixels meet the inclusion threshold The algorithm implementation assists medical professionals in better analyzing patient brain conditions and provides researchers with enhanced insights into brain structure and functionality. Typical code structures involve pixel queue management, boundary detection, and iterative region expansion loops with predefined homogeneity criteria.