On digital distance transforms in three dimensions
Computer Vision and Image Understanding
Efficient Skeletonization of Volumetric Objects
IEEE Transactions on Visualization and Computer Graphics
Skeleton-based morphological coding of binary images
IEEE Transactions on Image Processing
Automatic extraction of the topology of 3D electrical mock-ups using a mixed octree-voxel method
Advances in Engineering Software
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This paper presents an image processing approach for information extraction from three-dimensional (3-D) images of vasculature. It extracts quantitative information such as skeleton, length, diameter, and vessel-to-tissue ratio for different vessels as well as their branches. Furthermore, it generates 3-D visualization of vessels based on desired anatomical characteristics such as vessel diameter or 3-D connectivity. Steps of the proposed approach are: (1) pre-processing, (2) distance mappings, (3) branch labeling, (4) quantification, and (5) visualization. We have tested and evaluated the proposed algorithms using simulated images of multi-branch vessels and real confocal microscopic images of the vessels in rat brains. Experimental results illustrate performance of the methods and usefulness of the results for medical image analysis applications.