High-resolution micro{CT scanners now exist for imaging small animals. In particular, such a scanner can generate very large three-dimensional (3D) digital images of the rat's hepatic vasculature. These images provide data on the overall structure and function of such complex vascular trees. Unfortunately, human operators have extreme diÆculty in extracting the extensive vasculature contained in the images. Also, no suitable tree representation exists that permits straightforward structural analysis and information retrieval. This work proposes an automatic procedure for extracting and representing such a vascular tree. The procedure is both computation and memory{eÆcient and runs on current PCs. As results demonstrate, the procedure faithfully follows human-de ned measurements and provides far more information than can be de ned interactively.
[1]
William E. Higgins,et al.
Symmetric region growing
,
2003,
Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).
[2]
Rangachar Kasturi,et al.
Machine vision
,
1995
.
[3]
Xiaobo Li,et al.
Adaptive image region-growing
,
1994,
IEEE Trans. Image Process..
[4]
M. Dahlbom,et al.
Investigation of LSO crystals for high spatial resolution positron emission tomography
,
1996,
1996 IEEE Nuclear Science Symposium. Conference Record.
[5]
W.E. Higgins,et al.
System for analyzing high-resolution three-dimensional coronary angiograms
,
1996,
IEEE Trans. Medical Imaging.
[6]
Richard A. Robb.
Three-Dimensional Biomedical Imaging: Principles and Practice
,
1995
.