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Longitudinal Image Registration with Non-uniform Appearance Change

  • Istvan Csapo
  • Brad Davis
  • Yundi Shi
  • Mar Sanchez
  • Martin Styner
  • Marc Niethammer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7512)

Abstract

Longitudinal imaging studies are frequently used to investigate temporal changes in brain morphology. Image intensity may also change over time, for example when studying brain maturation. However, such intensity changes are not accounted for in image similarity measures for standard image registration methods. Hence, (i) local similarity measures, (ii) methods estimating intensity transformations between images, and (iii) metamorphosis approaches have been developed to either achieve robustness with respect to intensity changes or to simultaneously capture spatial and intensity changes. For these methods, longitudinal intensity changes are not explicitly modeled and images are treated as independent static samples. Here, we propose a model-based image similarity measure for longitudinal image registration in the presence of spatially non-uniform intensity change.

Keywords

Mutual Information Image Registration Transformation Model Gray Matter Intensity Target Image 
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 2012

Authors and Affiliations

  • Istvan Csapo
    • 1
  • Brad Davis
    • 3
  • Yundi Shi
    • 1
  • Mar Sanchez
    • 4
  • Martin Styner
    • 1
  • Marc Niethammer
    • 1
    • 2
  1. 1.University of North Carolina at Chapel HillUSA
  2. 2.Biomedical Research Imaging CenterUNC Chapel HillUSA
  3. 3.Kitware, Inc.CarrboroUSA
  4. 4.Emory UniversityAtlantaUSA

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