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Graph-based Deformable Image Registration

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Handbook of Biomedical Imaging

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.

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Notes

  1. 1.

    Fast-PD is available at http://cvc-komodakis.centrale-ponts.fr/.

  2. 2.

    http://www.cma.mgh.harvard.edu/ibsr/data.html

  3. 3.

    DRAMMS is available at http://www.nitrc.org/projects/dramms/.

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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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    1. Section of Biomedical Image Analysis, Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, USA

      A. Sotiras & C. Davatzikos

    2. Athinoula A. Martinos Center for Medical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, USA

      Y. Ou

    3. Center for Visual Computing, Department of Applied Mathematics, Ecole Centrale Paris, Paris, France

      N. Paragios

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    1. Department of Applied Mathematics, École Centrale de Paris / INRIA Saclay, Île De France, Chatenay-Malabry, France

      Nikos Paragios

    2. Department of Biomedical Engineering, Diagnostic Radiology and Electrical Engineering, Yale University, New Haven, Connecticut, USA

      James Duncan

    3. INRIA Sophia Antipolis - Méditerranée, Sophia Antipolis Cedex, France

      Nicholas Ayache

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    Sotiras, A., Ou, Y., Paragios, N., Davatzikos, C. (2015). Graph-based Deformable Image Registration. In: Paragios, N., Duncan, J., Ayache, N. (eds) Handbook of Biomedical Imaging. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-09749-7_18

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