Classification-driven pathological neuroimage retrieval using statistical asymmetry measures

Y. Liu, F. Dellaert, W. E. Rothfus, A. Moore, J. Schneider, T. Kanade

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Scopus citations

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 find the most-discriminative feature subset through image classification according to predefined semantic categories. Finally, this selected feature subset is usedas indexing features to retrieve medically similar images under a semantic-based image retrieval framework. Quantitative evaluations are provided.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2001 - 4th International Conference, Proceedings
EditorsWiro J. Niessen, Max A. Viergever
PublisherSpringer Verlag
Pages655-665
Number of pages11
ISBN (Print)3540426973, 9783540454687
DOIs
StatePublished - Jan 1 2001
Event4th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2001 - Utrecht, Netherlands
Duration: Oct 14 2001Oct 17 2001

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume2208
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other4th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2001
CountryNetherlands
CityUtrecht
Period10/14/0110/17/01

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Liu, Y., Dellaert, F., Rothfus, W. E., Moore, A., Schneider, J., & Kanade, T. (2001). Classification-driven pathological neuroimage retrieval using statistical asymmetry measures. In W. J. Niessen, & M. A. Viergever (Eds.), Medical Image Computing and Computer-Assisted Intervention - MICCAI 2001 - 4th International Conference, Proceedings (pp. 655-665). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 2208). Springer Verlag. https://doi.org/10.1007/3-540-45468-3_79