Sparse Bayesian Registration

  • Loïc Le Folgoc
  • Hervé Delingette
  • Antonio Criminisi
  • Nicholas Ayache
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)


We propose a Sparse Bayesian framework for non-rigid registration. Our principled approach is flexible, in that it efficiently finds an optimal, sparse model to represent deformations among any preset, widely overcomplete range of basis functions. It addresses open challenges in state-of-the-art registration, such as the automatic joint estimate of model parameters (e.g. noise and regularization levels). We demonstrate the feasibility and performance of our approach on cine MR, tagged MR and 3D US cardiac images, and show state-of-the-art results on benchmark datasets evaluating accuracy of motion and strain.


Relevance Vector Machine Multiscale Representation Pyramidal Scheme Sparse Bayesian Learning Sparse Regression 
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.
    Simpson, I.J., Schnabel, J.A., Groves, A.R., Andersson, J.L., Woolrich, M.W.: Probabilistic inference of regularisation in non-rigid registration. NeuroImage 59(3), 2438–2451 (2012)CrossRefGoogle Scholar
  2. 2.
    Janoos, F., Risholm, P., Wells III, W.: Bayesian characterization of uncertainty in multi-modal image registration. In: Dawant, B.M., Christensen, G.E., Fitzpatrick, J.M., Rueckert, D. (eds.) WBIR 2012. LNCS, vol. 7359, pp. 50–59. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  3. 3.
    Richard, F.J., Samson, A.M., Cuénod, C.A.: A SAEM algorithm for the estimation of template and deformation parameters in medical image sequences. Stat. Comput. 19(4) (2009)Google Scholar
  4. 4.
    Shi, W., Jantsch, M., Aljabar, P., Pizarro, L., Bai, W., Wang, H., O’Regan, D., Zhuang, X., Rueckert, D.: Temporal sparse free-form deformations. MedIA 17(7), 779–789 (2013)Google Scholar
  5. 5.
    Tipping, M.E.: Sparse bayesian learning and the relevance vector machine. JMLR 1 (2001)Google Scholar
  6. 6.
    Tipping, M.E., Faul, A.C., et al.: Fast marginal likelihood maximisation for sparse bayesian models. In: Workshop on Artificial Intelligence and Statistics, vol. 1 (January 2003)Google Scholar
  7. 7.
    De Craene, M., Marchesseau, S., Heyde, B., Gao, H., Alessandrini, M., Bernard, O., Piella, G., Porras, A., Saloux, E., Tautz, L., et al.: 3d strain assessment in ultrasound (STRAUS): A synthetic comparison of five tracking methodologies. TMI (2013)Google Scholar
  8. 8.
    Tobon-Gomez, C., De Craene, M., McLeod, K., Tautz, L., Shi, W., Hennemuth, A., Prakosa, A., Wang, H., Carr-White, G., Kapetanakis, S., et al.: Benchmarking framework for myocardial tracking and deformation algorithms: An open access database. MedIA (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Loïc Le Folgoc
    • 1
  • Hervé Delingette
    • 1
  • Antonio Criminisi
    • 2
  • Nicholas Ayache
    • 1
  1. 1.Asclepios Research ProjectINRIA Sophia AntipolisFrance
  2. 2.Machine Learning and Perception Group, Microsoft Research CambridgeUK

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