Graph-based deformable image registration

A. Sotiras, Y. Ou, N. Paragios, C. Davatzikos

Research output: Chapter in Book/Report/Conference proceedingChapter

4 Scopus citations


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 intensitybased 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.

Original languageEnglish (US)
Title of host publicationHandbook of Biomedical Imaging
Subtitle of host publicationMethodologies and Clinical Research
PublisherSpringer US
Number of pages29
ISBN (Electronic)9780387097497
ISBN (Print)9780387097480
StatePublished - Jan 1 2015

ASJC Scopus subject areas

  • Computer Science(all)
  • Medicine(all)
  • Biochemistry, Genetics and Molecular Biology(all)


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