Automatic 3D vascular tree construction in CT angiography

References 

1. Weber DM, Molloi SY, Folts JD, Peppler WW, Mistretta CA. Geometric quantitative coronary arteriography—a comparison of unsubtracted and dual energy-subtracted images. Invest Radiol. 1991;26:649–654
   • MEDLINE
   • CrossRef
2. Molloi S, Kassab G, Zhou Y. Quantification of coronary lumen volume by digital angiography: in-vivo validation. Circulation. 2001;104:1–7
3. Robb RA. Three-dimensional biomedical bimaging. Weinheim: VCH; 1995;
4. http://mipgsun.mipg.upenn.edu, date accessed August 18, 2002.
5. Udupa JK. 3D imaging in medicine. Boca Raton: CRC Press; 1991;
6. Wink O, Niessen WJ, Viergever MA. Fast delineation and visualization of vessels in 3D angiographic images. IEEE Trans Med Imag. 2000;19(4):337–346
7. Sijbers J, Scheunders P, Verhoye M, Linden A, Dyck AD, Raman E. Watershed-based segmentation of 3D MR data for volume quantization. Magn Reson Imag. 1997;15(6):679–688
8. Hibbard LS. Maximum a posteriori segmentation for medical visualization. In:  Vemuri B editors. Workshop on Biomedical Image Analysis. Santa Barbara: IEEE Inc; 1998;p. 93–102
9. Ballester MA, Zisserman A, Brady M. Combined statistical and geometrical 3D segmentation and measurement of brain structure in Workshop on Biomedical Image Analysis. In:  Vemuri B editors. Santa Barbara: IEEE Inc; 1998;p. 14–23
10. Kottke DP, Sun Y. Segmentation of coronary arteriograms by iterative ternary classification. IEEE Trans Biomed Engng. 1990;37(8):778–785
11. Ma C, Wan S. Parallel thinning algorithms on 3D (18,6) binary images. Computer Vision and Image Understanding. 2000;80(3):364–378
12. Ma C, Wan S. A medial-surface oriented 3D two-subfield thinning algorithm. Pattern Recogn Lett. 2001;22(13):1439–1446
13. Nikolaidis N, Pitas I. 3D image processing algorithm. London: Wiley; 2001;
14. Palagyi K, Kuba A. A 3D 6-subiteration thinning algorithm for extracting medial lines. Pattern Recogn Lett. 1998;19:613–627
15. Bertrand G. A parallel thinning algorithm for medial surfaces. Pattern Recogn Lett. 1995;16:979–986
16. Ma C, Sonka M. A fully parallel 3D thinning algorithm and its applications. Comput Vis Image Understand. 1996;64(3):420–433
17. Saha PK, Rosenfeld A. Determining simplicity and computing topological change in strongly normal partial tillings of R2 or R3. Pattern Recogn. 2000;33:105–118
18. Saha PK, Chaudhuri BB. Detection of 3D simple points for topology preserving transformations with applications to thinning. IEEE Trans Pat Anal Mach Intell. 1994;16(10):1028–1031
19. Borgefors G, Nystrom I, Baja GD. Computing skeletons in three dimensions. Pattern Recogn. 1999;32:1225–1236
20. Saha PK, Chaudhuri BB, Majumder DD. A new shape preserving parallel thinning algorithm for 3D digital images. Pattern Recogn. 1997;30:1939–1955
21. Chuang JH, Tsai CH, Ko MC. Skeletonization of three-dimensional object using generalized potential field. IEEE Trans PAMI. 2000;22(11):1241–1251
22. Arcelli C, Baja GSD. A width-independent fast thinning algorithm. IEEE Trans PAMI. 1985;7(4):463–474
23. Stefano CD, Frucci M. Spatial relations among pattern subsets as a guide for skeleton pruning in visual forms. Berlin: Springer; 2001; p. 155–64
24. Shaked D, Bruckstein AM. Pruning medial axes. Comput Vis Image Understand. 1998;69:156–169
25. Kasyanov VN, Evstigneev VA. Graph theory for programmers. Dordrecht: Kluwer; 2000;
26. http://www.mathworks.com, date accessed August 18, 2002.