Simultaneous Geometric - Iconic Registration

  • Aristeidis Sotiras
  • Yangming Ou
  • Ben Glocker
  • Christos Davatzikos
  • Nikos Paragios
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6362)


In this paper, we introduce a novel approach to bridge the gap between the landmark-based and the iconic-based voxel-wise registration methods. The registration problem is formulated with the use of Markov Random Field theory resulting in a discrete objective function consisting of thee parts. The first part of the energy accounts for the iconic-based volumetric registration problem while the second one for establishing geometrically meaningful correspondences by optimizing over a set of automatically generated mutually salient candidate pairs of points. The last part of the energy penalizes locally the difference between the dense deformation field due to the iconic-based registration and the implied displacements due to the obtained correspondences. Promising results in real MR brain data demonstrate the potentials of our approach.


Markov Random Field Candidate Point Point Correspondence Registration Problem Pairwise Potential 
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 2010

Authors and Affiliations

  • Aristeidis Sotiras
    • 1
    • 2
  • Yangming Ou
    • 3
  • Ben Glocker
    • 4
  • Christos Davatzikos
    • 3
  • Nikos Paragios
    • 1
    • 2
  1. 1.Laboratoire MASEcole Centrale de ParisFrance
  2. 2.Equipe GALENINRIA Saclay - Île de FranceFrance
  3. 3.Section of Biomedical Image Analysis (SBIA)University of PennsylvaniaUSA
  4. 4.Computer Aided Medical Procedures (CAMP)Technische Universität MünchenGermany

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