Seed-invariant region growing: its properties and applications to 3-D medical CT images

The objective of image segmentation is to define disjoint regions of interest from a digital image. The region-growing approach among many segmentation methods employs connectivity, local homogeneity, and other image-dependent characteristics as features for segmentation. A three-dimensional CT (computed tomography) image can be formed by imaging a contrast-injected subject. The spreading scenario is similar to region growing. The intensity degradation along the contrast-spreading paths requires local-homogeneity information for better segmentation. We present the properties of a symmetric region-growing (SymRG) approach that is suitable for processing medical CT images. We review the concept and definitions of SymRG, describe its seed-invariant property and computational separability. These significant factors govern the region-growing behavior (unidirectional region growing and inter-slice merging) and computational and memory-usage efficiency. We also propose a general SymRG algorithm for any dimensional images and demonstrate experimental results.