Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures

  • Y. Liu
  • F. Dellaert
  • W. E. Rothfus
  • A. Moore
  • J. Schneider
  • T. Kanade
Conference paper

DOI: 10.1007/3-540-45468-3_79

Part of the Lecture Notes in Computer Science book series (LNCS, volume 2208)
Cite this paper as:
Liu Y., Dellaert F., Rothfus W.E., Moore A., Schneider J., Kanade T. (2001) Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures. In: Niessen W.J., Viergever M.A. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001. MICCAI 2001. Lecture Notes in Computer Science, vol 2208. Springer, Berlin, Heidelberg

Abstract

This paper reports our methodology and initial results on volumetric pathological neuroimage retrieval. A set of novel image features are computed to quantify the statistical distributions of approximate bilateral asymmetry of normal and pathological human brains. We apply memory-based learning method to findt he most-discriminative feature subset through image classification according to predefined semantic categories. Finally, this selected feature subset is used as indexing features to retrieve medically similar images under a semantic-based image retrieval framework. Quantitative evaluations are provided.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Y. Liu
    • 1
  • F. Dellaert
    • 1
  • W. E. Rothfus
    • 2
  • A. Moore
    • 1
  • J. Schneider
    • 1
  • T. Kanade
    • 1
  1. 1.The Robotics InstituteCarnegie Mellon UniversityPittsburghUSA
  2. 2.University of Pittsburgh Medical CenterPittsburgh

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