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Computer Vision and Image Understanding
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
A Statistical Assembled Model for Segmentation of Entire 3D Vasculature
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 04
Computers in Biology and Medicine
ViVa: the virtual vascular project
IEEE Transactions on Information Technology in Biomedicine
Construction of a human topological model from medical data
IEEE Transactions on Information Technology in Biomedicine
Rapid automated three-dimensional tracing of neurons from confocal image stacks
IEEE Transactions on Information Technology in Biomedicine
A review on MR vascular image processing: skeleton versus nonskeleton approaches: part II
IEEE Transactions on Information Technology in Biomedicine
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Although many deformable models have been proposed in medical applications for segmenting isolated structures in the human anatomy, not much of such work had been done on tubular structures such as the vasculature. In this paper, we propose a statistical assembled model for tubular structures (SAMTUS) to segment entire tubular structure from three-dimensional (3D) volumetric data. To our knowledge, there is no literature about the statistical deformable model for entire tubular structures. Specifically, the statistical tubular model is composed of a statistical axis model (SAM) and a statistical surface model (SSM). Both of them are assembled from a set of branch segments through the control points. Instead of searching for fuzzy correspondence along tubular axes or surfaces, we build point matching between feature points along tubular segments, and train SAM and SSM independently to characterize, respectively, the axial and the cross-sectional variation of the entire structure. In this way, more accurate point correspondence can be established, and a larger number of deformation modes can be captured. Our SAMTUS-based segmentation process consists of three stages: initialization, model fitting and final refinement. The experimental results demonstrate that the algorithm obtains good quantifications on the morphology and volume of the vasculature of the zebrafish which is being used increasingly as a specimen for drug screening and genomic research.