Discriminative MR image feature analysis for automatic schizophrenia and Alzheimer's disease classification

Yanxi Liu, Leonid Teverovskiy, Owen Carmichael, Ron Kikinis, Martha Shenton, Cameron S. Carter, V. Andrew Stenger, Simon Davis, Howard Aizenstein, James T. Becker, Oscar L. Lopez, Carolyn C. Meltzer

Research output: Contribution to journalConference articlepeer-review

73 Scopus citations


We construct a computational framework for automatic central nervous system (CNS) disease discrimination using high resolution Magnetic Resonance Images (MRI) of human brains. More than 3000 MR image features are extracted, forming a high dimensional coarse-to-fine hierarchical image description that quantifies brain asymmetry, texture and statistical properties in corresponding local regions of the brain. Discriminative image feature subspaces are computed, evaluated and selected automatically. Our initial experimental results show 100% and 90% separability between chronicle schizophrenia (SZ) and first episode SZ versus their respective matched controls. Under the same computational framework, we also find higher than 95% separability among Alzheimer's Disease, mild cognitive impairment patients, and their matched controls. An average of 88% classification success rate is achieved using leave-one-out cross validation on five different well-chosen patient-control image sets of sizes from 15 to 27 subjects per disease class.

Original languageEnglish (US)
Pages (from-to)393-401
Number of pages9
JournalLecture Notes in Computer Science
Issue numberPART 1
StatePublished - 2004
EventMedical Image Computing and Computer-Assisted Intervention, MICCAI 2004 - 7th International Conference, Proceedings - Saint-Malo, France
Duration: Sep 26 2004Sep 29 2004

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • General Computer Science


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