Morphometric analysis of brain structures for improved discrimination

Li Shen, James Ford, Fillia Makedon, Yuhang Wang, Tilmann Steinberg, Song Ye, Andrew Saykin

Research output: Contribution to journalConference articlepeer-review

8 Scopus citations


We perform discriminative analysis of brain structures using morphometric information. Spherical harmonics technique and point distribution model are used for shape description. Classification is performed using linear discriminants and support vector machines with several feature selection approaches. We consider both inclusion and exclusion of volume information in the discrimination. We perform extensive experimental studies by applying different combinations of techniques to hippocampal data in schizophrenia and achieve best jackknife classification accuracies of 95% (whole set) and 90% (right-handed males), respectively. Our results find that the left hippocampus is a better predictor than the right in the complete dataset, but that the right hippocampus is a stronger predictor than the left in the right-handed male subset. We also propose a new method for visualization of discriminative patterns.

Original languageEnglish (US)
Pages (from-to)513-520
Number of pages8
JournalLecture Notes in Computer Science
Issue numberPART 2
StatePublished - 2003
EventMedical Image Computing and Computer-Assisted Intervention, MICCAI 2003 - 6th International Conference Proceedings - Montreal, Que., Canada
Duration: Nov 15 2003Nov 18 2003

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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