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Temporally-Dependent Image Similarity Measure for Longitudinal Analysis

  • Conference paper

Part of the Lecture Notes in Computer Science book series (LNIP,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

  • White Matter
  • Transformation Model
  • Registration Method
  • Registration Error
  • White Matter Intensity

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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© 2012 Springer-Verlag Berlin Heidelberg

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Csapo, I., Davis, B., Shi, Y., Sanchez, M., Styner, M., Niethammer, M. (2012). Temporally-Dependent Image Similarity Measure for Longitudinal Analysis. In: Dawant, B.M., Christensen, G.E., Fitzpatrick, J.M., Rueckert, D. (eds) Biomedical Image Registration. WBIR 2012. Lecture Notes in Computer Science, vol 7359. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31340-0_11

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  • DOI: https://doi.org/10.1007/978-3-642-31340-0_11

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-31339-4

  • Online ISBN: 978-3-642-31340-0

  • eBook Packages: Computer ScienceComputer Science (R0)