Cerebral Vascular Tree Matching of 3D-RA Data Based on Tree Edit Distance

  • W. H. Tang
  • Albert C. S. Chung
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4091)


In this paper, we present a novel approach to matching cerebral vascular trees obtained from 3D-RA data-sets based on minimization of tree edit distance. Our approach is fully automatic which requires zero human intervention. Tree edit distance is a term used in the field of theoretical computer science to describe the similarity between two labeled trees. In our approach, we abstract the geometry and morphology of vessel branches into the labels of tree nodes and then use combinatorial optimization strategies to compute the approximated edit distance between the trees. Once the optimal tree edit distance is computed, the spatial correspondences between the vessels can be established. By visual inspection to the experimental results, we find that our approach is accurate.


Intracranial Aneurysm Voronoi Diagram Edit Distance Theoretical Tree Short Path Tree 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Beck, J., Rohde, S., Berkefeld, J., Seifert, V., Raabe, A.: Size and location of ruptured and unruptured intracranial aneurysms measured by 3-dimensional rotational angiography. Surg. Neurol. 65, 18–25 (2006)CrossRefGoogle Scholar
  2. 2.
    Weir, B., Disney, L., Karrison, T.: Sizes of ruptured and unruptured aneurysms in relation to their sites and the ages of patients. J. Neurosurg. 96, 64–70 (2002)CrossRefGoogle Scholar
  3. 3.
    Antiga, L., Steinman, D.A.: Robust and objective decomposition and mapping of bifurcating vessels. IEEE Trans. on Med. Img. 23, 704–713 (2004)CrossRefGoogle Scholar
  4. 4.
    Cool, D., Chillet, D., Kim, J., Guyon, J.P., Foskey, M., Aylward, S.: Tissue-Based Affine Registration of Brain Images to form a Vascular Density Atlas. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 9–15. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Chillet, D., Jomier, J., Cool, D., Aylward, S.: Vascular atlas formation using a vessel-to-image affine registration method. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2878, pp. 335–342. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  6. 6.
    Jomier, J., Aylward, S.R.: Rigid and deformable vasculature-to-image registration: A hierarchical approach. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 829–836. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  7. 7.
    Shasha, D., Wang, T.L., Zhang, K., Shih, F.Y.: Exact and approximate algorithms for unordered tree matching. IEEE Trans on Systems, Man, and Cybernetics 24, 668–678 (1994)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • W. H. Tang
    • 1
  • Albert C. S. Chung
    • 1
  1. 1.Lo Kwee-Seong Medical Image Analysis Laboratory, Department of Computer Science and EngineeringThe Hong Kong University of Science and TechnologyKowloon, Hong Kong

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