Hierarchical manifold learning

Med Image Comput Comput Assist Interv. 2012;15(Pt 1):512-9. doi: 10.1007/978-3-642-33415-3_63.

Abstract

We present a novel method of hierarchical manifold learning which aims to automatically discover regional variations within images. This involves constructing manifolds in a hierarchy of image patches of increasing granularity, while ensuring consistency between hierarchy levels. We demonstrate its utility in two very different settings: (1) to learn the regional correlations in motion within a sequence of time-resolved images of the thoracic cavity; (2) to find discriminative regions of 3D brain images in the classification of neurodegenerative disease,

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms
  • Alzheimer Disease / diagnosis
  • Alzheimer Disease / pathology
  • Artificial Intelligence
  • Automation
  • Brain / pathology
  • Heart / physiology
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Imaging, Three-Dimensional / methods*
  • Models, Statistical
  • Neurodegenerative Diseases / diagnosis*
  • Neurodegenerative Diseases / pathology
  • Pattern Recognition, Automated / methods
  • Reproducibility of Results
  • Software
  • Thoracic Cavity / pathology*
  • Time Factors