TY - JOUR
T1 - A Bayesian morphometry algorithm
AU - Herskovits, Edward H.
AU - Peng, Hanchuan
AU - Davatzikos, Christos
N1 - Funding Information:
Manuscript received July 28, 2003; rebised February 20, 2004. This work was supported in part by the Human Brain Project under Grant AG13743, which is funded by the National Institute of Aging, the National Cancer Institute, and the National Institute of Mental Health. The work of E. H. Herskovits was supported in part by a Richard S. Ross Clinician Scientist Award from Johns Hopkins University. *E. H. Herskovits is with the Department of Radiology, University of Pennsylvania, 3600 Market Street, Suite 370, Room 117, Philadelphia, PA 19104 USA (e-mail: ehh@ieee.org).
PY - 2004/6
Y1 - 2004/6
N2 - Most methods for structure-function analysis of the brain in medical images are usually based on voxel-wise statistical tests performed on registered magnetic resonance (MR) images across subjects. A major drawback of such methods is the inability to accurately locate regions that manifest nonlinear associations with clinical variables. In this paper, we propose Bayesian morphological analysis methods, based on a Bayesian-network representation, for the analysis of MR brain images. First, we describe how Bayesian networks (BNs) can represent probabilistic associations among voxels and clinical (function) variables. Second, we present a model-selection framework, which generates a BN that captures structure-function relationships from MR brain images and function variables. We demonstrate our methods in the context of determining associations between regional brain atrophy (as demonstrated on MR images of the brain), and functional deficits. We employ two data sets for this evaluation: the first contains MR images of 11 subjects, where associations between regional atrophy and a functional deficit are almost linear; the second data set contains MR images of the ventricles of 84 subjects, where the structure-function association is nonlinear. Our methods successfully identify voxel-wise morphological changes that are associated with functional deficits in both data sets, whereas standard statistical analysis (i.e., t-test and paired t-test) fails in the nonlinear-association case.
AB - Most methods for structure-function analysis of the brain in medical images are usually based on voxel-wise statistical tests performed on registered magnetic resonance (MR) images across subjects. A major drawback of such methods is the inability to accurately locate regions that manifest nonlinear associations with clinical variables. In this paper, we propose Bayesian morphological analysis methods, based on a Bayesian-network representation, for the analysis of MR brain images. First, we describe how Bayesian networks (BNs) can represent probabilistic associations among voxels and clinical (function) variables. Second, we present a model-selection framework, which generates a BN that captures structure-function relationships from MR brain images and function variables. We demonstrate our methods in the context of determining associations between regional brain atrophy (as demonstrated on MR images of the brain), and functional deficits. We employ two data sets for this evaluation: the first contains MR images of 11 subjects, where associations between regional atrophy and a functional deficit are almost linear; the second data set contains MR images of the ventricles of 84 subjects, where the structure-function association is nonlinear. Our methods successfully identify voxel-wise morphological changes that are associated with functional deficits in both data sets, whereas standard statistical analysis (i.e., t-test and paired t-test) fails in the nonlinear-association case.
KW - Bayes procedures
KW - Bayesian network
KW - Computational anatomy
KW - Image analysis
KW - Image classification
KW - Morphology-function analysis
KW - Voxel-based morphometry
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U2 - 10.1109/TMI.2004.826949
DO - 10.1109/TMI.2004.826949
M3 - Article
C2 - 15191147
AN - SCOPUS:2942511605
SN - 0278-0062
VL - 23
SP - 723
EP - 737
JO - IEEE transactions on medical imaging
JF - IEEE transactions on medical imaging
IS - 6
ER -