Temporally-Dependent Image Similarity Measure for Longitudinal Analysis

  • Istvan Csapo
  • Brad Davis
  • Yundi Shi
  • Mar Sanchez
  • Martin Styner
  • Marc Niethammer
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7359)

Abstract

Current longitudinal image registration methods rely on the assumption that image appearance between time-points remains constant or changes uniformly within intensity classes. This assumption, however, is not valid for magnetic resonance imaging of brain development. Registration methods developed to align images with non-uniform appearance change either (i) locally minimize some global similarity measure, or (ii) iteratively estimate an intensity transformation that makes the images similar. However, these methods treat the individual images as independent static samples and are inadequate for the strong non-uniform appearance changes seen in neurodevelopmental data. Here, we propose a model-based similarity measure intended for aligning longitudinal images that locally estimates a temporal model of intensity change. Unlike previous approaches, the model-based formulation is able to capture complex appearance changes between time-points and we demonstrate that it is critical when using a deformable transformation model.

Keywords

Cross Correlation Tempo Estima 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

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

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