Bayesian clustering methods for morphological analysis of MR images

Hanckuan Peng; Edward Herskovits; Christos Davatzikos

doi:10.1109/ISBI.2002.1029399

Bayesian clustering methods for morphological analysis of MR images

Hanckuan Peng
, Edward Herskovits
, Christos Davatzikos

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

7 Scopus citations

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 language	English (US)
Title of host publication	2002 IEEE International Symposium on Biomedical Imaging, ISBI 2002 - Proceedings
Publisher	IEEE Computer Society
Pages	875-878
Number of pages	4
ISBN (Electronic)	078037584X
DOIs	https://doi.org/10.1109/ISBI.2002.1029399
State	Published - 2002
Externally published	Yes
Event	IEEE International Symposium on Biomedical Imaging, ISBI 2002 - Washington, United States Duration: Jul 7 2002 → Jul 10 2002

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
Volume	2002-January
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Other

Other	IEEE International Symposium on Biomedical Imaging, ISBI 2002
Country/Territory	United States
City	Washington
Period	7/7/02 → 7/10/02

ASJC Scopus subject areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Access to Document

10.1109/ISBI.2002.1029399

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). Article 1029399 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2002-January). IEEE Computer Society. https://doi.org/10.1109/ISBI.2002.1029399

Bayesian clustering methods for morphological analysis of MR images. / Peng, Hanckuan; Herskovits, Edward; Davatzikos, Christos.
2002 IEEE International Symposium on Biomedical Imaging, ISBI 2002 - Proceedings. IEEE Computer Society, 2002. p. 875-878 1029399 (Proceedings - International Symposium on Biomedical Imaging; Vol. 2002-January).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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., 1029399, Proceedings - International Symposium on Biomedical Imaging, vol. 2002-January, IEEE Computer Society, pp. 875-878, IEEE International Symposium on Biomedical Imaging, ISBI 2002, Washington, United States, 7/7/02. https://doi.org/10.1109/ISBI.2002.1029399

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AB - 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.

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Bayesian clustering methods for morphological analysis of MR images

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