Advertisement

Simultaneous Geometric - Iconic Registration

  • Aristeidis Sotiras
  • Yangming Ou
  • Ben Glocker
  • Christos Davatzikos
  • Nikos Paragios
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6362)

Abstract

In this paper, we introduce a novel approach to bridge the gap between the landmark-based and the iconic-based voxel-wise registration methods. The registration problem is formulated with the use of Markov Random Field theory resulting in a discrete objective function consisting of thee parts. The first part of the energy accounts for the iconic-based volumetric registration problem while the second one for establishing geometrically meaningful correspondences by optimizing over a set of automatically generated mutually salient candidate pairs of points. The last part of the energy penalizes locally the difference between the dense deformation field due to the iconic-based registration and the implied displacements due to the obtained correspondences. Promising results in real MR brain data demonstrate the potentials of our approach.

Keywords

Markov Random Field Candidate Point Point Correspondence Registration Problem Pairwise Potential 
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.
    Joshi, S., Miller, M.: Landmark matching via large deformation diffeomorphisms. In: IEEE TIP (2000)Google Scholar
  2. 2.
    Shen, D., Davatzikos, C.: Hammer: hierarchical attribute matching mechanism for elastic registration. In: IEEE TMI (2002)Google Scholar
  3. 3.
    Chui, H., Rangarajan, A.: A new point matching algorithm for non-rigid registration. In: CVIU (2003)Google Scholar
  4. 4.
    Beg, M.F., Miller, M.I., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. CVIU (2005)Google Scholar
  5. 5.
    Rueckert, D., Aljabar, P., Heckemann, R.A., Hajnal, J.V., Hammers, A.: Diffeomorphic registration using b-splines. In: Larsen, R., Nielsen, M., Sporring, J. (eds.) MICCAI 2006. LNCS, vol. 4191, pp. 702–709. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  6. 6.
    Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through mrfs and efficient linear programming. In: MedIA (2008)Google Scholar
  7. 7.
    Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: Efficient non-parametric image registration. NeuroImage (2009)Google Scholar
  8. 8.
    Johnson, H., Christensen, G.: Consistent landmark and intensity-based image registration. In: IEEE TMI (2002)Google Scholar
  9. 9.
    Cachier, P., Mangin, J.F., Pennec, X., Riviere, D., Papadopoulos-Orfanos, D., Règis, J.: Multisubject non-rigid registration of brain MRI using intensity and geometric features. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, p. 734. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  10. 10.
    Azar, A., Xu, C., Pennec, X., Ayache, N.: An interactive hybrid non-rigid registration framework for 3d medical images. In: IEEE ISBI (2006)Google Scholar
  11. 11.
    Biesdorf, A., Wörz, S., Kaiser, H.J., Stippich, C., Rohr, K.: Hybrid spline-based multimodal registration using local measures for joint entropy and mutual information. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5761, pp. 607–615. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  12. 12.
    Hellier, P., Barillot, C.: Coupling dense and landmark-based approaches for nonrigid registration. In: IEEE TMI (2003)Google Scholar
  13. 13.
    Papademetris, X., Jakowski, A.P., Schultz, R.T., Staib, L.H., Duncan, J.S.: 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
  14. 14.
    Zhan, Y., Shen, D.: Deformable segmentation of 3-d ultrasound prostate images using statistical texture matching method. In: IEEE TMI (2006)Google Scholar
  15. 15.
    Ou, Y., Davatzikos, C.: Dramms: Deformable registration via attribute matching and mutual-saliency weighting. In: Prince, J.L., Pham, D.L., Myers, K.J. (eds.) IPMI 2009. LNCS, vol. 5636, pp. 50–62. Springer, Heidelberg (2009)Google Scholar
  16. 16.
    Kolmogorov, V.: Convergent tree-reweighted message passing for energy minimization. IEEE PAMI 28 (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Aristeidis Sotiras
    • 1
    • 2
  • Yangming Ou
    • 3
  • Ben Glocker
    • 4
  • Christos Davatzikos
    • 3
  • Nikos Paragios
    • 1
    • 2
  1. 1.Laboratoire MASEcole Centrale de ParisFrance
  2. 2.Equipe GALENINRIA Saclay - Île de FranceFrance
  3. 3.Section of Biomedical Image Analysis (SBIA)University of PennsylvaniaUSA
  4. 4.Computer Aided Medical Procedures (CAMP)Technische Universität MünchenGermany

Personalised recommendations