Multi-modality canonical feature selection for Alzheimer's disease diagnosis.

Journal Article (Journal Article)

Feature selection has been commonly regarded as an effective method to lessen the problem of high dimension and low sample size in medical image analysis. In this paper, we propose a novel multimodality canonical feature selection method. Unlike the conventional sparse Multi-Task Learning (MTL) based feature selection method that mostly considered only the relationship between target response variables, we further consider the correlations between features of different modalities by projecting them into a canonical space determined by canonical correlation analysis. We call the projections as canonical representations. By setting the canonical representations as regressors in a sparse least square regression framework and by further penalizing the objective function with a new canonical regularizer on the weight coefficient matrix, we formulate a multi-modality canonical feature selection method. With the help of the canonical information of canonical representations and also a canonical regularizer, the proposed method selects canonical-cross-modality features that are useful for the tasks of clinical scores regression and multi-class disease identification. In our experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we combine Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images to jointly predict clinical scores of Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) and also identify multiclass disease status for Alzheimer's disease diagnosis.

Full Text

Duke Authors

Cited Authors

  • Zhu, X; Suk, H-I; Shen, D

Published Date

  • 2014

Published In

  • Med Image Comput Comput Assist Interv

Volume / Issue

  • 17 / Pt 2

Start / End Page

  • 162 - 169

PubMed ID

  • 25485375

Pubmed Central ID

  • PMC4465097

Digital Object Identifier (DOI)

  • 10.1007/978-3-319-10470-6_21

Language

  • eng

Conference Location

  • Germany