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Teichmüller shape space theory and its application to brain morphometry

  • Yalin Wang
  • , Wei Dai
  • , Xianfeng Gu
  • , Tony F. Chan
  • , Shing Tung Yau
  • , Arthur W. Toga
  • , Paul M. Thompson

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

15 Scopus citations

Abstract

Here we propose a novel method to compute Teichmüller shape space based shape index to study brain morphometry. Such a shape index is intrinsic, and invariant under conformal transformations, rigid motions and scaling. We conformally map a genus-zero open boundary surface to the Poincaré disk with the Yamabe flow method. The shape indices that we compute are the lengths of a special set of geodesics under hyperbolic metric. Tests on longitudinal brain imaging data were used to demonstrate the stability of the derived feature vectors. In leave-one-out validation tests, we achieved 100% accurate classification (versus only 68% accuracy for volume measures) in distinguishing 11 HIV/AIDS individuals from 8 healthy control subjects, based on Teichmüller coordinates for lateral ventricular surfaces extracted from their 3D MRI scans.

Original languageEnglish
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI2009 - 12th International Conference, Proceedings
Pages133-140
Number of pages8
EditionPART 2
DOIs
StatePublished - 2009
Externally publishedYes
Event12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009 - London, United Kingdom
Duration: 20 Sep 200924 Sep 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume5762 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference12th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2009
Country/TerritoryUnited Kingdom
CityLondon
Period20/09/0924/09/09

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