Shape analysis with multivariate tensor-based morphometry and holomorphic differentials

Yalin Wang, Tony F. Chan, Arthur W. Toga, Paul M. Thompson

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

2 Scopus citations

Abstract

In this paper, we propose multivariate tensor-based surface morphometry, a new method for surface analysis, using holomorphic differentials; we also apply it to study brain anatomy. Differential forms provide a natural way to parameterize 3D surfaces, but the multivariate statistics of the resulting surface metrics have not previously been investigated. We computed new statistics from the Riemannian metric tensors that retain the full information in the deformation tensor fields. We present the canonical holomorphic one-forms with improved numerical accuracy and computational efficiency. We applied this framework to 3D MRI data to analyze hippocampal surface morphometry in Alzheimer's Disease (AD; 12 subjects), lateral ventricular surface morphometry in HIV/AIDS (11 subjects) and biomarkers in lateral ventricles in HIV/AIDS (11 subjects). Experimental results demonstrated that our method powerfully detected brain surface abnormalities. Multivariate statistics on the local tensors outperformed other TBM methods including analysis of the Jacobian determinant, the largest eigenvalue, or the pair of eigenvalues, of the surface Jacobian matrix.

Original languageEnglish (US)
Title of host publication2009 IEEE 12th International Conference on Computer Vision, ICCV 2009
Pages2349-2356
Number of pages8
DOIs
StatePublished - Dec 1 2009
Externally publishedYes
Event12th International Conference on Computer Vision, ICCV 2009 - Kyoto, Japan
Duration: Sep 29 2009Oct 2 2009

Publication series

NameProceedings of the IEEE International Conference on Computer Vision

Other

Other12th International Conference on Computer Vision, ICCV 2009
CountryJapan
CityKyoto
Period9/29/0910/2/09

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition

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