Automatic Point Landmark Matching for Regularizing Nonlinear Intensity Registration: Application to Thoracic CT Images

  • Martin Urschler
  • Christopher Zach
  • Hendrik Ditt
  • Horst Bischof
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4191)


Nonlinear image registration is a prerequisite for a variety of medical image analysis tasks. A frequently used registration method is based on manually or automatically derived point landmarks leading to a sparse displacement field which is densified in a thin-plate spline (TPS) framework. A large problem of TPS interpolation/approximation is the requirement for evenly distributed landmark correspondences over the data set which can rarely be guaranteed by landmark matching algorithms. We propose to overcome this problem by combining the sparse correspondences with intensity-based registration in a generic nonlinear registration scheme based on the calculus of variations. Missing landmark information is compensated by a stronger intensity term, thus combining the strengths of both approaches. An explicit formulation of the generic framework is derived that constrains an intra-modality intensity data term with a regularization term from the corresponding landmarks and an anisotropic image-driven displacement regularization term. An evaluation of this algorithm is performed comparing it to an intensity- and a landmark-based method. Results on four synthetically deformed and four clinical thorax CT data sets at different breathing states are shown.


Regularization Term Point Landmark Nonlinear Registration Intensity Registration Landmark Match 
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.
    Maintz, J., Viergever, M.: A Survey of Medical Image Registration. Medical Image Analysis 2(1), 1–36 (1998)CrossRefGoogle Scholar
  2. 2.
    Crum, W.R., Hartkens, T., Hill, D.L.G.: Non-rigid image registration: theory and practice. The British Journal of Radiology 77, 140–153 (2004)CrossRefGoogle Scholar
  3. 3.
    Rohr, K.: Landmark-Based Image Analysis Using Geometric and Intensity Models. Computational Imaging and Vision. Kluwer Academic Publishers, Dordrecht (2001)Google Scholar
  4. 4.
    Bookstein, F.: Principal Warps: Thin-Plate Splines and the Decomposition of Deformations. IEEE PAMI 11(6), 567–585 (1989)MATHGoogle Scholar
  5. 5.
    Urschler, M., Bauer, J., Ditt, H., Bischof, H.: SIFT and Shape Context for Feature-Based Nonlinear Registration of Thoracic CT Images. In: Proc. Computer Vision Applications to Medical Image Analysis (to appear, 2006)Google Scholar
  6. 6.
    Gee, J.C.: Probabilistic Matching of Deformed Images. PhD thesis, Univ. Pennsylvania, Philadelphia (1996)Google Scholar
  7. 7.
    Johnson, H.J., Christensen, G.E.: Consistent Landmark and Intensity-Based Image Registration. IEEE TMI 21(5), 450–461 (2002)Google Scholar
  8. 8.
    Li, B., Christensen, G.E., Fill, J., Hoffman, E.A., Reinhardt, J.M.: 3D Inter-Subject Warping and Registration of Pulmonary CT Images for a Human Lung Model. In: Proc. SPIE Conf. on Medical Imaging, vol. 4683, pp. 324–335 (2002)Google Scholar
  9. 9.
    Fan, L., Chen, C.W.: 3D Warping and Registration from Lung Images. In: Proc. SPIE Conf. on Medical Imaging, vol. 3660, pp. 459–470 (1999)Google Scholar
  10. 10.
    Hellier, P., Barillot, C.: Coupling Dense and Landmark Based Approaches for Nonrigid Registration. IEEE TMI 22(2), 217–227 (2003)Google Scholar
  11. 11.
    Liu, T., Shen, D., Davatzikos, C.: Deformable registration of cortical structures via hybrid volumetric and surface warping. NeuroImage 22, 1790–1801 (2004)CrossRefGoogle Scholar
  12. 12.
    Papademetris, X., Jackowski, A.P., Schultz, R.T., Staib, L.H.: S., D.J.: Integrated Intensity and Point-Feature Nonrigid Registration. In: Barillot, C., Haynor, D.R., Hellier, P. (eds.) MICCAI 2004. LNCS, vol. 3216, pp. 763–770. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  13. 13.
    Modersitzki, J.: Numerical Methods for Image Registration. Oxford University Press, Oxford (2004)MATHGoogle Scholar
  14. 14.
    Mikolajczyk, K., Schmid, C.: Performance Evaluation of Local Descriptors. IEEE PAMI 27(10), 1615–1630 (2005)Google Scholar
  15. 15.
    Lowe, D.G.: Distinctive Image Features from Scale-Invariant Keypoints. Int J Computer Vision 60(2), 91–110 (2004)CrossRefGoogle Scholar
  16. 16.
    Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE PAMI 24(4), 509–522 (2002)Google Scholar
  17. 17.
    Weickert, J., Schnörr, C.: A Theoretical Framework for Convex Regularizers in PDE-Based Computation of Image Motion. Int. J. Computer Vision 45(3), 245–264 (2001)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Martin Urschler
    • 1
  • Christopher Zach
    • 2
  • Hendrik Ditt
    • 3
  • Horst Bischof
    • 1
  1. 1.Institute for Computer Graphics & VisionGraz University of TechnologyAustria
  2. 2.VRVis Research CentreGrazAustria
  3. 3.Siemens Medical Solutions, CTE PAForchheimGermany

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