An automated supervised method for the diagnosis of Alzheimer’s disease based on fMRI data using weighted voting schemes

Author

Tripoliti, Evathia E. ; Fotiadis, Dimitrios I. ; Argyropoulou, Maria

Author_Institution

Dept. of Comput. Sci., Univ. of Ioannina, Ioannina

fYear

2008

fDate

10-12 Sept. 2008

Firstpage

340

Lastpage

345

Abstract

We present an automated supervised method which assists in the diagnosis of Alzheimerpsilas disease (AD) using fMRI data. The method consists of five stages: a) preprocessing of fMRI data to remove motion and spatial noise artifacts, b) modeling of the data using generalized linear models (GLM), c) feature extraction, d) feature selection and e) classification using majority and weighted voting schemes.

Keywords

biomedical MRI; diseases; feature extraction; image classification; medical image processing; neurophysiology; Alzheimer disease; automated supervised method; feature classification; feature extraction; feature selection; functional magnetic resonance imaging; generalized linear models; weighted voting schemes; Alzheimer´s disease; Biomedical imaging; Data mining; Feature extraction; Magnetic resonance imaging; Medical diagnostic imaging; Positron emission tomography; Senior citizens; Testing; Voting; Alzheimer’s disease; Random Forests; functional MRI; weighted voting;

fLanguage

English

Publisher

ieee

Conference_Titel

Imaging Systems and Techniques, 2008. IST 2008. IEEE International Workshop on

Conference_Location

Crete

Print_ISBN

978-1-4244-2496-2

Electronic_ISBN

978-1-4244-2497-9

Type

conf

DOI

10.1109/IST.2008.4659997

Filename

4659997