Metric-Based Pairwise and Multiple Image Registration

  • Qian Xie
  • Sebastian Kurtek
  • Eric Klassen
  • Gary E. Christensen
  • Anuj Srivastava
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8690)


Registering pairs or groups of images is a widely-studied problem that has seen a variety of solutions in recent years. Most of these solutions are variational, using objective functions that should satisfy several basic and desired properties. In this paper, we pursue two additional properties – (1) invariance of objective function under identical warping of input images and (2) the objective function induces a proper metric on the set of equivalence classes of images – and motivate their importance. Then, a registration framework that satisfies these properties, using the L 2-norm between a novel representation of images, is introduced. Additionally, for multiple images, the induced metric enables us to compute a mean image, or a template, and perform joint registration. We demonstrate this framework using examples from a variety of image types and compare performances with some recent methods.


metric-based registration elastic image deformation post-registration analysis mean image multiple registration 


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Qian Xie
    • 1
  • Sebastian Kurtek
    • 2
  • Eric Klassen
    • 1
  • Gary E. Christensen
    • 3
  • Anuj Srivastava
    • 1
  1. 1.Florida State UniversityTallahasseeUnited States
  2. 2.Ohio State UniversityColumbusUnited States
  3. 3.University of IowaIowa CityUnited States

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