Classification of structural MRI images in Alzheimer's disease from the perspective of ill-posed problems.

ABSTRACT:

Background

Machine learning neuroimaging researchers have often relied on regularization techniques when classifying MRI images. Although these were originally introduced to deal with "ill-posed" problems it is rare to find studies that evaluate the ill-posedness of MRI image classification problems. In addition, to avoid the effects of the "curse of dimensionality" very often dimension reduction is applied to the data.

Methodology

Baseline structural MRI data from cognitively normal and Alzheimer's disease (AD) patients from the AD Neuroimaging Initiative database were used in this study. We evaluated here the ill-posedness of this classification problem across different dimensions and sample sizes and its relationship to the performance of regularized logistic regression (

SUBMITTER: Casanova R 

PROVIDER: S-EPMC3468621 | biostudies-literature | 2012

REPOSITORIES: biostudies-literature