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Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001

Volume 2208 of the series Lecture Notes in Computer Science pp 655-665

Date:

Classification-Driven Pathological Neuroimage Retrieval Using Statistical Asymmetry Measures

  • Y. LiuAffiliated withThe Robotics Institute, Carnegie Mellon University
  • , F. DellaertAffiliated withThe Robotics Institute, Carnegie Mellon University
  • , W. E. RothfusAffiliated withUniversity of Pittsburgh Medical Center
  • , A. MooreAffiliated withThe Robotics Institute, Carnegie Mellon University
  • , J. SchneiderAffiliated withThe Robotics Institute, Carnegie Mellon University
  • , T. KanadeAffiliated withThe Robotics Institute, Carnegie Mellon University

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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.