Geometric-Feature-Based Spectral Graph Matching in Pharyngeal Surface Registration

  • Qingyu Zhao
  • Stephen Pizer
  • Marc Niethammer
  • Julian Rosenman
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


Fusion between an endoscopic movie and a CT can aid specifying the tumor target volume for radiotherapy. That requires a deformable pharyngeal surface registration between a 3D endoscope reconstruction and a CT segmentation. In this paper, we propose to use local geometric features for deriving a set of initial correspondences between two surfaces, with which an association graph can be constructed for registration by spectral graph matching. We also define a new similarity measurement to provide a meaningful way for computing inter-surface affinities in the association graph. Our registration method can deal with large non-rigid anatomical deformation, as well as missing data and topology change. We tested the robustness of our method with synthetic deformations and showed registration results on real data.


Partial Surface Registration Error Initial Link Spectral Graph Surface Registration 
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.


  1. 1.
    Allen, B., Curless, B., Popovic, Z.: The space of human body shapes: reconstruction and parameterization from range scans. In: ACM SIGGRAPH, vol. 22, pp. 587–594 (2003)Google Scholar
  2. 2.
    Li, H., Luo, L., Vlasic, D., Peers, P., Popovic, J., Pauly, M., Rusinkiewicz, S.: Temporally coherent completion of dynamic shapes. ACM Transactions on Graphics 31(2) (2012)Google Scholar
  3. 3.
    Huang, Q., Adams, B., Wicke, M., Guibas, L.: Non-rigid registration under isometric deformations. In: Symposium on Geometry Processing, pp. 1449–1457 (2008)Google Scholar
  4. 4.
    Zeng, W., Gu, X.D.: Registration for 3d surfaces with large deformations using quasi-conformal curvature flow. In: Computer Vision and Pattern Recognition, pp. 2457–2464 (2011)Google Scholar
  5. 5.
    Lipman, Y., Funkhouser, T.: Möbius voting for surface correspondence. ACM Transactions on Graphics 28(72) (2009)Google Scholar
  6. 6.
    Zigelman, G., Kimmel, R., Kiryati, N.: On bending invariant signatures for surfaces. IEEE Trans. Vis. Comput. Graph. 8(2), 198–207 (2002)CrossRefGoogle Scholar
  7. 7.
    Sharma, A., Horaud, R.: Shape matching based on diffusion embedding and on mutual isometric consistency. In: IEEE CVPRW, pp. 29–36 (2010)Google Scholar
  8. 8.
    Johnson, A.E., Hebert, M.: Using spin images for efficient object recognition in cluttered 3d scenes. IEEE Transactions on PAMI 21(5), 433–449 (1999)CrossRefGoogle Scholar
  9. 9.
    Sun, J., Ovsjanikov, M., Guibas, L.: A concise and provably informative multi-scale signature based on heat diffusion. In: Eurographics Symposium on Geometry Processing 2009, vol. 28, pp. 1383–1392 (2009)Google Scholar
  10. 10.
    Lombaert, H., Sporring, J., Siddiqi, K.: Diffeomorphic spectral matching of cortical surfaces. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 376–389. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  11. 11.
    Koenderink, J.: Solid Shape. The MIT Press (1990)Google Scholar
  12. 12.
    Lombaert, H., Grady, L., Polimeni, J., Cheriet, F.: Fast brain matching with spectral correspondence. In: Székely, G., Hahn, H.K. (eds.) IPMI 2011. LNCS, vol. 6801, pp. 660–673. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Qingyu Zhao
    • 1
  • Stephen Pizer
    • 1
  • Marc Niethammer
    • 1
  • Julian Rosenman
    • 2
  1. 1.Computer ScienceUNC Chapel HillUnited States
  2. 2.Radiation OncologyUNC Chapel HillUnited States

Personalised recommendations