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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)

Abstract

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.

Keywords

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.

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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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