Bayesian clustering methods for morphological analysis of MR images

Hanckuan Peng, Edward Herskovits, Christos Davatzikos

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

Abstract

Determining the relationship between structure (i.e. morphology) and function is a fundamental problem in brain research. In this paper we present a new framework based on Bayesian clustering methods for the voxel-wise statistical morphology-function analysis of registered MR images. We construct a Bayesian network to automatically identify the significant associations between voxel-wise morphological variables and functional variables, such as cognitive performance. A Bayesian latent variable induction method is applied to locate the homogeneous association regions on registered maps of morphological variables. Experimental results on images with simulated atrophy confirm that the new method outperforms conventional statistical method, based on linear statistics.

Original languageEnglish (US)
Title of host publication2002 IEEE International Symposium on Biomedical Imaging, ISBI 2002 - Proceedings
PublisherIEEE Computer Society
Pages875-878
Number of pages4
ISBN (Electronic)078037584X
DOIs
StatePublished - Jan 1 2002
EventIEEE International Symposium on Biomedical Imaging, ISBI 2002 - Washington, United States
Duration: Jul 7 2002Jul 10 2002

Publication series

NameProceedings - International Symposium on Biomedical Imaging
Volume2002-January
ISSN (Print)1945-7928
ISSN (Electronic)1945-8452

Other

OtherIEEE International Symposium on Biomedical Imaging, ISBI 2002
CountryUnited States
CityWashington
Period7/7/027/10/02

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ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Peng, H., Herskovits, E., & Davatzikos, C. (2002). Bayesian clustering methods for morphological analysis of MR images. In 2002 IEEE International Symposium on Biomedical Imaging, ISBI 2002 - Proceedings (pp. 875-878). [1029399] (Proceedings - International Symposium on Biomedical Imaging; Vol. 2002-January). IEEE Computer Society. https://doi.org/10.1109/ISBI.2002.1029399