Multi-scale Vessel Boundary Detection

  • Hüseyin Tek
  • Alper Ayvacı
  • Dorin Comaniciu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3765)


In this paper, we present a robust and accurate method for the segmentation of cross-sectional boundaries of vessels found in contrast-enhanced images. The proposed algorithm first detects the edges along 1D rays in multiple scales by using mean-shift analysis. Second, edges from different scales are accurately and efficiently combined by using the properties of mean-shift clustering. Third, boundaries of vessel cross-sections are obtained by using local and global perceptual edge grouping and elliptical shape verification. The proposed algorithm is stable to (i) the case where the vessel is surrounded by other vessels or other high contrast structures, (iii) contrast variations in vessel boundary, and (iii) variations in the vessel size and shape. The accuracy of the algorithm is shown on several examples.


Compute Tomography Angiography Magnetic Resonance Angiography Curve Segment Edge Element Edge Grouping 
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  1. 1.
    Aylward, S., Bullitt, E.: Initialization, noise, singularities, and scale in height-ridge traversal for tubular object centerline extraction. IEEE Trans. on Medical Imaging 21(2), 61–75 (2002)CrossRefGoogle Scholar
  2. 2.
    Behrens, T., Rohr, K., Stiehl, H.: Robust segmentation of tubular structures in 3-d medical images by parametric object detection and tracking. IEEE Transactions on Systems, Man, and Cybernetics, Part B 33(4), 554–561 (2003)CrossRefGoogle Scholar
  3. 3.
    Comaniciu, D.: An algorithm for data-driven bandwidth selection. IEEE Trans. Pattern Analysis Machine Intell. 25(2), 281–288 (2003)CrossRefGoogle Scholar
  4. 4.
    Comaniciu, D., Meer, P.: Mean shift: A robust approach toward feature space analysis. IEEE Trans. PAMI 24(5), 603–619 (2002)Google Scholar
  5. 5.
    Descoteaux, M., Collins, L., Siddiqi, K.: Geometric flows for segmenting vasculature in MRI: Theory and validation. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 500–507. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  6. 6.
    Guy, G., Medioni, G.: Inferring global perceptual contours from local features. IJCV 20(1), 113–133 (1996)CrossRefGoogle Scholar
  7. 7.
    Hernandez-Hoyos, M., Anwander, A., Orkisz, M., Roux, J.P., Doueck, I.E.M.P.: A deformable vessel model with single point initialization for segmentation, quantification and visualization of blood vessesl in 3D MRA. In: Delp, S.L., DiGoia, A.M., Jaramaz, B. (eds.) MICCAI 2000. LNCS, vol. 1935, pp. 735–745. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  8. 8.
    Kimia, B.B., Frankel, I., Popescu, A.-M.: Euler spiral for shape completion. Int. J. Comput. Vision 54(1-3), 157–180 (2003)CrossRefGoogle Scholar
  9. 9.
    Krissian, K., Malandain, G., Ayache, N., Vaillant, R., Trousset, Y.: Model based multiscale detection of 3d vessels. In: IEEE Conf. CVPR, pp. 722–727 (1998)Google Scholar
  10. 10.
    Mumford, D.: Elastica and computer vision. Algebraic Geometry and Its Applications, 491–506 (1994)Google Scholar
  11. 11.
    Parent, P., Zucker, S.W.: Trace inference, curvature consistency and curve detection. PAMI 11(8), 823–839 (1989)Google Scholar
  12. 12.
    Sarkar, S., Boyer, K.: Perceptual organization using Bayesian networks. In: CVPR, pp. 251–256 (1992)Google Scholar
  13. 13.
    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 visualisation of curvilinear structures in medical images. Med. Image Analysis 2(2), 143–168 (1998)CrossRefGoogle Scholar
  14. 14.
    Sha’ashua, A., Ullman, S.: Structural saliency: The detection of globally salient structures using a locally connected network. In: ICCV (1988)Google Scholar
  15. 15.
    Staib, L.H., Duncan, J.S.: Boundary finding with parametrically deformable models. PAMI 14(2), 1061–1075 (1992)Google Scholar
  16. 16.
    Tek, H., Comaniciu, D., Williams, J.: Vessel detection by mean shift based ray propagation. In: Work. on Math. Models in Biomedical Image Analysis (2001)Google Scholar
  17. 17.
    Ullman, S.: Filling-in the gaps: The shape of subjective contours and a model for their generation. Biological Cybernetics 25, 1–6 (1976)MathSciNetGoogle Scholar
  18. 18.
    Wink, O., Niessen, W., Viergever, M.A.: Fast delination and visualization of vessels in 3-D angiographic images. IEEE Trans. on Medical Imaging 19, 337–345 (2000)CrossRefGoogle Scholar
  19. 19.
    Wink, O., Niessen, W.J., Viergever, M.A.: Multiscale vessel tracking. IEEE Trans. on Medical Imaging 23(1), 130–133 (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Hüseyin Tek
    • 1
  • Alper Ayvacı
    • 1
  • Dorin Comaniciu
    • 1
  1. 1.Siemens Corporate ResearchPrincetonUSA

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