Inverse-Consistent Symmetric Free Form Deformation

  • Marc Modat
  • M. Jorge Cardoso
  • Pankaj Daga
  • David Cash
  • Nick C. Fox
  • Sébastien Ourselin
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7359)


Bias in image registration has to be accounted for when performing morphometric studies. The presence of bias can lead to unrealistic power estimates and can have an adverse effect in group separation studies. Most image registration algorithms are formulated in an asymmetric fashion and the solution is biased towards the transformation direction. The popular free-form deformation algorithm has been shown to be a robust and accurate method for medical image registration. However, it suffers from the lack of symmetry which could potentially bias the result. This work presents a symmetric and inverse-consistent variant of the free form deformation.

We first assess the proposed framework in the context of segmentation-propagation. We also applied it to longitudinal images to assess regional volume change. In both evaluations, the symmetric algorithm outperformed a non-symmetric formulation of the free-form deformation.


Image Registration Normalise Mutual Information Forward Transformation Segmentation Propagation Image Registration Algorithm 
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

  • Marc Modat
    • 1
  • M. Jorge Cardoso
    • 1
  • Pankaj Daga
    • 1
  • David Cash
    • 2
  • Nick C. Fox
    • 2
  • Sébastien Ourselin
    • 1
    • 2
  1. 1.Centre for Medical Imaging Computing, Department of Medical Physics, and BioengineeringUniversity College LondonUK
  2. 2.Dementia Research Centre, Institute of NeurologyUniversity College LondonUK

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