TY - JOUR
T1 - Towards a Holistic Cortical Thickness Descriptor
T2 - Heat Kernel-Based Grey Matter Morphology Signatures
AU - the Alzheimer's Disease Neuroimaging Initiative
AU - Wang, Gang
AU - Wang, Yalin
N1 - Publisher Copyright:
© 2016
PY - 2017/2/15
Y1 - 2017/2/15
N2 - In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, on publicly available data from the Alzheimer's Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis.
AB - In this paper, we propose a heat kernel based regional shape descriptor that may be capable of better exploiting volumetric morphological information than other available methods, thereby improving statistical power on brain magnetic resonance imaging (MRI) analysis. The mechanism of our analysis is driven by the graph spectrum and the heat kernel theory, to capture the volumetric geometry information in the constructed tetrahedral meshes. In order to capture profound brain grey matter shape changes, we first use the volumetric Laplace-Beltrami operator to determine the point pair correspondence between white-grey matter and CSF-grey matter boundary surfaces by computing the streamlines in a tetrahedral mesh. Secondly, we propose multi-scale grey matter morphology signatures to describe the transition probability by random walk between the point pairs, which reflects the inherent geometric characteristics. Thirdly, a point distribution model is applied to reduce the dimensionality of the grey matter morphology signatures and generate the internal structure features. With the sparse linear discriminant analysis, we select a concise morphology feature set with improved classification accuracies. In our experiments, the proposed work outperformed the cortical thickness features computed by FreeSurfer software in the classification of Alzheimer's disease and its prodromal stage, i.e., mild cognitive impairment, on publicly available data from the Alzheimer's Disease Neuroimaging Initiative. The multi-scale and physics based volumetric structure feature may bring stronger statistical power than some traditional methods for MRI-based grey matter morphology analysis.
KW - Alzheimer's disease
KW - Computer-Aided Diagnosis
KW - Heat Kernel
KW - Magnetic resonance imaging (MRI)
KW - Shape analysis
UR - http://www.scopus.com/inward/record.url?scp=85007071332&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85007071332&partnerID=8YFLogxK
U2 - 10.1016/j.neuroimage.2016.12.014
DO - 10.1016/j.neuroimage.2016.12.014
M3 - Article
C2 - 28033566
AN - SCOPUS:85007071332
SN - 1053-8119
VL - 147
SP - 360
EP - 380
JO - NeuroImage
JF - NeuroImage
ER -