Combining Front Propagation with Shape Knowledge for Accurate Curvilinear Modelling

1. Aylward, S., Bullitt, E.: Initialization, Noise, Singularities and Scale in Height Ridge Traversal for Tubular Object Centerline Extraction. IEEE TMI 21(2), 61–75 (2002)

   Google Scholar 

2. Blum, H.: A transformation for extracting new descriptors of shape. In: Wathen, D. (ed.) Models for the Perception of Speech and Visual Form, pp. 363–380. MIT Press, Cambridge (1967)

   Google Scholar 

3. Borgefors, G.: Distance transformations in arbitrary dimensions. Comput. Vision Graphics Image Process 27, 321–345 (1984)

   Article  Google Scholar 

4. Chalana, V., Costa, W., Kim, Y.: Integrating region growing and edge detection using regularization. In: SPIE, vol. 2434, pp. 262–271 (1995)

   Google Scholar 

5. Cohen, L., Cohen, I.: Finite-Element Methods for Active Contour Models and Balloons for 2-D and 3-D Images. IEEE PAMI 15(11), 1131–1147 (1993)

   Google Scholar 

6. Deschamps, T., Cohen, L.D.: Fast extraction of minimal path in 3D images and applications to virtual endoscopy. Medical Image Analysis 5, 281–299 (2001)

   Article  Google Scholar 

7. Dijkstra, E.W.: A note on two problems in connexion with graphs. Numerische Mathematik 1, 269–271 (1959)

   Article  MATH  MathSciNet  Google Scholar 

8. Frangi, A.F., Niessen, W.J., Vinken, K., Viergever, M.A.: Multi-scale vessel enhancement filtering. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 130–137. Springer, Heidelberg (1998)

   Google Scholar 

9. Krissian, K., Malandain, G., Ayache, N., Vaillant, R., Trousset, Y.: Model-Based Detection of Tubular Structures in 3D Images. CVIU 80, 130–171 (2000)

   MATH  Google Scholar 

10. Li, R., Brown, S., Wilson, L., Young, J., Luo, S.: Progressively Refined Patient- Specific Vessel System Models from Generic Representations. In: DICTA 2002, Melbourne, Australia, October 2001, pp. 184–189 (2001)

    Google Scholar 

11. Lohmann, G.: Volumetric Image Analysis. Wiley, Chichester (1998)

    MATH  Google Scholar 

12. Nikolaidis, N., Pitas, I.: 3-D Image Processing Algorithms. John Wiley, Chichester (2001)

    Google Scholar 

13. Pizer, S.M., Thall, A.L., Chen, D.T.: M-reps: A New Object Representation for Graphics. Technical Report TR99-030, Uni. N. Carolina, Chapel Hill (September 1999)

    Google Scholar 

14. Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., Gerig, G., Kikinis, R.: Three-Dimensional Multi-Scale Line Filter for Segmentation and Visualization of Curvilinear Structures in Medical Images. Medical Image Analysis 2(2), 143–168 (1998)

    Article  Google Scholar 

15. Sethian, J.A.: A fast marching level set method for monotonically advancing fronts. Proc. of the National Academy of Sciences of the USA 93(4), 1591–1595 (1996)

    Article  MATH  MathSciNet  Google Scholar 

16. Sethian, J.A.: Level set methods: Evolving interfaces in geometry, fluid mechanics, computer vision, and materials science. Cambridge monographs on applied and computational mathematics, vol. 3. Cambridge University Press, Cambridge (1999)

    MATH  Google Scholar 

17. Terzopoulos, D., Witkin, A., Kass, M.: Constraints on deformable models: Recovering 3D shape and nonrigid motion. Artif. Int. 36(1), 91–123 (1988)

    Article  MATH  Google Scholar 

18. Truyen, R., Deschamps, T., Cohen, L.D.: Clinical Evaluation of an Automatic Path Tracker for Virtual Colonoscopy. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 169–176. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

19. Wink, O., Niessen, W.J., Viergever, M.: Fast Delineation and Visualization of Vessels in 3-D Angiographic Images. IEEE TMI 19(4), 337–346 (2000)

    Google Scholar 

20. Yim, P.J., Choyke, P.L., Summers, R.M.: Gray-Scale Skeletonisation of Small Vessels in Magnetic Resonance Angiography. IEEE TMI 19(6), 568–576 (2000)

    Google Scholar 

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