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

Publication ,  Conference
Liu, Y; Teverovskiy, L; Carmichael, O; Kikinis, R; Shenton, M; Carter, CS; Stenger, VA; Davis, S; Aizenstein, H; Becker, JT; Lopez, OL; Meltzer, CC
Published in: Lecture Notes in Computer Science
January 1, 2004

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. © Springer-Verlag Berlin Heidelberg 2004.

Duke Scholars

Published In

Lecture Notes in Computer Science

DOI

ISSN

0302-9743

Publication Date

January 1, 2004

Volume

3216

Issue

PART 1

Start / End Page

393 / 401

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences
 

Citation

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Liu, Y., Teverovskiy, L., Carmichael, O., Kikinis, R., Shenton, M., Carter, C. S., … Meltzer, C. C. (2004). Discriminative MR image feature analysis for automatic schizophrenia and Alzheimer's disease classification. In Lecture Notes in Computer Science (Vol. 3216, pp. 393–401). https://doi.org/10.1007/978-3-540-30135-6_48
Liu, Y., L. Teverovskiy, O. Carmichael, R. Kikinis, M. Shenton, C. S. Carter, V. A. Stenger, et al. “Discriminative MR image feature analysis for automatic schizophrenia and Alzheimer's disease classification.” In Lecture Notes in Computer Science, 3216:393–401, 2004. https://doi.org/10.1007/978-3-540-30135-6_48.
Liu Y, Teverovskiy L, Carmichael O, Kikinis R, Shenton M, Carter CS, et al. Discriminative MR image feature analysis for automatic schizophrenia and Alzheimer's disease classification. In: Lecture Notes in Computer Science. 2004. p. 393–401.
Liu, Y., et al. “Discriminative MR image feature analysis for automatic schizophrenia and Alzheimer's disease classification.” Lecture Notes in Computer Science, vol. 3216, no. PART 1, 2004, pp. 393–401. Scopus, doi:10.1007/978-3-540-30135-6_48.
Liu Y, Teverovskiy L, Carmichael O, Kikinis R, Shenton M, Carter CS, Stenger VA, Davis S, Aizenstein H, Becker JT, Lopez OL, Meltzer CC. Discriminative MR image feature analysis for automatic schizophrenia and Alzheimer's disease classification. Lecture Notes in Computer Science. 2004. p. 393–401.

Published In

Lecture Notes in Computer Science

DOI

ISSN

0302-9743

Publication Date

January 1, 2004

Volume

3216

Issue

PART 1

Start / End Page

393 / 401

Related Subject Headings

  • Artificial Intelligence & Image Processing
  • 46 Information and computing sciences