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Registration Using Sparse Free-Form Deformations

  • Wenzhe Shi
  • Xiahai Zhuang
  • Luis Pizarro
  • Wenjia Bai
  • Haiyan Wang
  • Kai-Pin Tung
  • Philip Edwards
  • Daniel Rueckert
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7511)

Abstract

Non-rigid image registration using free-form deformations (FFD) is a widely used technique in medical image registration. The balance between robustness and accuracy is controlled by the control point grid spacing and the amount of regularization. In this paper, we revisit the classic FFD registration approach and propose a sparse representation for FFDs using the principles of compressed sensing. The sparse free-form deformation model (SFFD) can capture fine local details such as motion discontinuities without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate smooth as well as discontinuous deformations in 2D and 3D image sequences. Compared to the classic FFD approach, a significant increase in registration accuracy can be observed in natural images (61%) as well as in cardiac MR images (53%) with discontinuous motions.

Keywords

Control Point Image Registration Sparse Representation Registration Accuracy Sparsity Constraint 
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-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Wenzhe Shi
    • 1
  • Xiahai Zhuang
    • 2
  • Luis Pizarro
    • 1
  • Wenjia Bai
    • 1
  • Haiyan Wang
    • 1
  • Kai-Pin Tung
    • 1
  • Philip Edwards
    • 1
  • Daniel Rueckert
    • 1
  1. 1.Biomedical Image Analysis GroupImperial College LondonUK
  2. 2.Shanghai Advanced Research InstituteChinese Academy of SciencesChina

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