A Hybrid Approach via SRG and IDE for Volume Segmentation
Li Wang, Xiaoan Tang, Junda Zhang, Dongdong Guan · IEICE Transactions on Information and Systems · 2017
Volume segmentation is of great significances for feature visualization and feature extraction, essentially volume segmentation can be viewed as generalized cluster. This paper proposes a hybrid approach via symmetric region growing (SRG) and information diffusion estimation (IDE) for volume segmentation, the volume dataset is over-segmented to series of subsets by SRG and then subsets are clustered by K-Means basing on distance-metric derived from IDE, experiments illustrate superiority of the hybrid approach with better segmentation performance.