Feature selection using factor analysis for Alzheimer's diagnosis using 18F-FDG PET images

Med Phys. 2010 Nov;37(11):6084-95. doi: 10.1118/1.3488894.

Abstract

Purpose: This article presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of Alzheimer's disease (AD). Two hundred and ten 18F-FDG PET images from the ADNI initiative [52 normal controls (NC), 114 mild cognitive impairment (MCI), and 53 AD subjects] are studied.

Methods: The proposed methodology is based on the selection of voxels of interest using the t-test and a posterior reduction of the feature dimension using factor analysis. Factor loadings are used as features for three different classifiers: Two multivariate Gaussian mixture model, with linear and quadratic discriminant function, and a support vector machine with linear kernel.

Results: An accuracy rate up to 95% when NC and AD are considered and an accuracy rate up to 88% and 86% for NC-MCI and NC-MCI,AD, respectively, are obtained using SVM with linear kernel.

Conclusions: Results are compared to the voxel-as-features and a PCA- based approach and the proposed methodology achieves better classification performance.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Aged, 80 and over
  • Alzheimer Disease / diagnosis*
  • Alzheimer Disease / diagnostic imaging*
  • Cognition Disorders / diagnosis*
  • Cognition Disorders / diagnostic imaging*
  • Diagnosis, Computer-Assisted
  • Fluorodeoxyglucose F18 / pharmacokinetics*
  • Humans
  • Image Processing, Computer-Assisted
  • Middle Aged
  • Models, Statistical
  • Multivariate Analysis
  • Normal Distribution
  • Positron-Emission Tomography / methods*
  • Radiopharmaceuticals / pharmacokinetics*
  • Reproducibility of Results

Substances

  • Radiopharmaceuticals
  • Fluorodeoxyglucose F18