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Graph-based Deformable Image Registration

  • A. SotirasEmail author
  • Y. Ou
  • N. Paragios
  • C. Davatzikos
Chapter

Abstract

Deformable image registration is a field that has received considerable attention in the medical image analysis community. As a consequence, there is an important body of works that aims to tackle deformable registration. In this chapter we review one class of these techniques that use discrete optimization, and more specifically Markov Random Field models. We begin the chapter by explaining how one can formulate the deformable registration problem as a minimal cost graph problem where the nodes of the graph corresponds to the deformation grid, the graph connectivity encodes regularization constraints, and the labels correspond to 3D displacements. We then explain the use of discrete models in intensity-based volumetric registration. In the third section, we detail the use of Gabor-based attribute vectors in the context of discrete deformable registration, demonstrating the versatility of the graph-based models. In the last section of the chapter, the case of landmark-based registration is discussed. We first explain the discrete graphical model behind establishing landmark correspondences, and then continue to show how one can integrate it with the intensity-based model towards creating enhanced models that combine the best of both worlds.

Keywords

Control Point Image Registration Transformation Model Similarity Criterion Registration Method 
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.

Notes

Acknowledgements

We would like to acknowledge Dr. Ben Glocker, from Imperial College London, whose work formed the basis of the subsequent works that are presented here.

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© Springer Science+Business Media New York 2015

Authors and Affiliations

  1. 1.Section of Biomedical Image Analysis, Center for Biomedical Image Computing and AnalyticsUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Athinoula A. Martinos Center for Medical ImagingMassachusetts General Hospital, Harvard Medical SchoolBostonUSA
  3. 3.Center for Visual Computing, Department of Applied MathematicsEcole Centrale ParisParisFrance

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