Joint estimation of multiple clinical variables of neurological diseases from imaging patterns

Yong Fan, Daniel Kaufer, Dinggang Shen

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

21 Citations (Scopus)

Abstract

This paper presents a method to estimate multiple clinical variables associated with neurological pathologies from brain images, aiming to quantitatively evaluate continuous transition of neurological pathologies from the normal to diseased state. Built upon morphological measures derived from structural MR brain images, a Bayesian regression method is developed to jointly model multiple clinical variables for capturing their inherent correlations and suppressing noise. Coupled with a feature selection technique, the regression method is used to build a joint estimator of multiple clinical variables associated with Alzheimer's disease from structural MR brain images of elderly individuals. The cross-validation results demonstrate that the proposed method has superior performance over existing techniques.

Original languageEnglish
Title of host publication2010 7th IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2010 - Proceedings
Pages852-855
Number of pages4
DOIs
Publication statusPublished - 2010
Externally publishedYes
Event7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Rotterdam, Netherlands
Duration: 2010 Apr 142010 Apr 17

Publication series

Name2010 7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010 - Proceedings

Other

Other7th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2010
Country/TerritoryNetherlands
CityRotterdam
Period10/4/1410/4/17

Keywords

  • ADAS-Cog
  • Alzheimer's disease
  • Bayesian regression
  • MMSE
  • Structural MR brain image

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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