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A Model of Volumetric Shape for the Analysis of Longitudinal Alzheimer’s Disease Data

  • Xinyang Liu
  • Xiuwen Liu
  • Yonggang Shi
  • Paul Thompson
  • Washington Mio
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6313)

Abstract

We develop a multi-scale model of shape based on a volumetric representation of solids in 3D space. A signed energy function (SEF) derived from the model is designed to quantify the magnitude of regional shape changes that correlate well with local shrinkage and expansion. The methodology is applied to the analysis of longitudinal morphological data representing hippocampal volumes extracted from one-year repeat magnetic resonance scans of the brain of 381 subjects collected by the Alzheimer’s Disease Neuroimaging Initiative. We first establish a strong correlation between the SEFs and hippocampal volume loss over a one-year period and then use SEFs to characterize specific regions where hippocampal atrophy over the one-year period differ significantly among groups of normal controls and subjects with mild cognitive impairment and Alzheimer’s disease.

Keywords

Mild Cognitive Impairment Hippocampal Volume Hippocampal Atrophy Left Hippocampus Shape Distance 
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 2010

Authors and Affiliations

  • Xinyang Liu
    • 1
  • Xiuwen Liu
    • 2
  • Yonggang Shi
    • 3
  • Paul Thompson
    • 3
  • Washington Mio
    • 1
  1. 1.Department of MathematicsFlorida State UniversityTallahasse
  2. 2.Department of Computer ScienceFlorida State UniversityTallahasse
  3. 3.Laboratory of NeuroImagingUCLA School of MedicineLos Angeles

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