Advertisement

Groupwise Diffeomorphic Non-rigid Registration for Automatic Model Building

  • T. F. Cootes
  • S. Marsland
  • C. J. Twining
  • K. Smith
  • C. J. Taylor
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3024)

Abstract

We describe a framework for registering a group of images together using a set of non-linear diffeomorphic warps. The result of the groupwise registration is an implicit definition of dense correspondences between all of the images in a set, which can be used to construct statistical models of shape change across the set, avoiding the need for manual annotation of training images. We give examples on two datasets (brains and faces) and show the resulting models of shape and appearance variation. We show results of experiments demonstrating that the groupwise approach gives a more reliable correspondence than pairwise matching alone.

Keywords

Face Image Appearance Model Large Mode Active Appearance Model Image Registration Method 
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.

References

  1. 1.
    Bajcsy, R., Lieberson, R., Reivich, M.: A computerized system for the elastic matching of deformed radiographic images to idealized atlas images. J. Comput. Assis. Tomogr. 7, 618–625 (1983)CrossRefGoogle Scholar
  2. 2.
    Baker, S., Matthews, I., Schneider, J.: Image coding with active appearance models. Technical Report CMU-RI-TR-03-13, Robotics Institute, Carnegie Mellon University, Pittsburgh, PA (April 2003)Google Scholar
  3. 3.
    Christensen, G.E., Joshi, S.C., Miller, M.: Volumetric transformation of brain anatomy. IEEE Trans. Medical Image 16, 864–877 (1997)CrossRefGoogle Scholar
  4. 4.
    Cootes, T.F., Edwards, G.J., Taylor, C.J.: Active appearance models. In: Burkhardt, H., Neumann, B. (eds.) ECCV 1998. LNCS, vol. 1407, pp. 484–498. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  5. 5.
    Cootes, T.F., Taylor, C.J., Cooper, D., Graham, J.: Active shape models - their training and application. Computer Vision and Image Understanding 61(1), 38–59 (1995)CrossRefGoogle Scholar
  6. 6.
    Davies, R.H., Twining, C.J., Cootes, T.F., Waterton, J.C., Taylor, C.J.: An information theoretic approach to statistical shape modelling. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2352, pp. 3–20. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  7. 7.
    Feldmar, J., Ayache, N.: Locally affine registration of free-form surfaces. In: CVPR 1994, pp. 496–501 (1994)Google Scholar
  8. 8.
    Jones, M.J., Poggio, T.: Multidimensional morphable models: A framework for representing and matching object classes. International Journal of Computer Vision 2(29), 107–131 (1998)zbMATHCrossRefGoogle Scholar
  9. 9.
    Lötjönen, J., Mäkelä, T.: Elastic matching using a deformation sphere. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 541–548. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
    Maes, F., Collignon, A., Vandermeulen, D., Marchal, G., Suetens, P.: Multimodality image registration by maximization of mutual information. IEEE Transactions on Medical Imaging 16(2), 187–198 (1997)CrossRefGoogle Scholar
  11. 11.
    Maintz, J.B.A., Viergever, M.A.: A survey of medical image registration. Medical Image Analysis 2(1), 1–36 (1998)CrossRefGoogle Scholar
  12. 12.
    Marsland, S., Twining, C.J.: Constructing data-driven optimal representations for iterative pairwise non-rigid registration. In: Gee, J.C., Maintz, J.B.A., Vannier, M.W. (eds.) WBIR 2003. LNCS, vol. 2717, pp. 50–60. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  13. 13.
    Marsland, S., Twining, C.J., Taylor, C.J.: Groupwise non-rigid registration using polyharmonic clamped-plate splines. In: Ellis, R.E., Peters, T.M. (eds.) MICCAI 2003. LNCS, vol. 2879, pp. 771–779. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  14. 14.
    McInerney, T., Terzopoulos, D.: Deformable models in medical image analysis: a survey. Medical Image Analysis 1(2), 91–108 (1996)CrossRefGoogle Scholar
  15. 15.
    Meier, D., Fisher, E.: Parameter space warping: Shape-based correspondence between morphologically different objects. IEEE Trans. Medical Image 21, 31–47 (2002)CrossRefGoogle Scholar
  16. 16.
    Messer, K., Matas, J., Kittler, J., Luettin, J., Maitre, G.: XM2VTSdb: The extended m2vts database. In: Proc. 2nd Conf. on Audio and Video-based Biometric Personal Verification. Springer, Heidelberg (1999)Google Scholar
  17. 17.
    Rissanen, J.: Stochastic Complexity in Statistical Inquiry. Series in Computer Science, vol. 15. World Scientific, Singapore (1989)zbMATHGoogle Scholar
  18. 18.
    Rueckert, D., Frangi, A.F., Schnabel, J.A.: Automatic construction of 3D statistical deformation models using non-rigid registration. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 77–84. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  19. 19.
    Studholme, C., Hill, C., Hawkes, D.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32, 71–86 (1999)CrossRefGoogle Scholar
  20. 20.
    Twining, C., Marsland, S., Taylor, C.: Measuring geodesic distances on the space of bounded diffeomorphisms. In: Rosin, P.L., Marshall, D. (eds.) 13th British Machine Vison Conference, September 2002, vol. 2, pp. 847–856. BMVA Press (2002)Google Scholar
  21. 21.
    Wang, Y., Staib, L.H.: Elastic model based non-rigid registration incorporating statistical shape information. In: Wells, W.M., Colchester, A.C.F., Delp, S.L. (eds.) MICCAI 1998. LNCS, vol. 1496, pp. 1162–1173. Springer, Heidelberg (1998)Google Scholar
  22. 22.
    Zitová, B., Flusser, J.: Image registration methods: A survey. Image and Vision Computing 21, 977–1000 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • T. F. Cootes
    • 1
  • S. Marsland
    • 1
  • C. J. Twining
    • 1
  • K. Smith
    • 1
  • C. J. Taylor
    • 1
  1. 1.Department of Imaging Science and Biomedical EngineeringUniversity of ManchesterManchesterUK

Personalised recommendations