By Topic

3D brain image-based diagnosis of Alzheimer's disease: Bringing medical vision into feature selection

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Eduardo Bicacro ; Institute for Systems and Robotics, Instituto Superior Técnico, Lisbon, Portugal ; Margarida Silveira ; Jorge S. Marques ; Durval C. Costa

Expert physicians are able to attain good Alzheimer's Disease (AD) diagnostic accuracy, relying on visual inspection of Positron Emission Tomography (PET) images only. Nevertheless, computerized methods have been implemented with similar or even better performance. We investigate the potential of the physician's experienced visual inspection to guide feature selection, in an automatic classification procedure. Eye tracking methodology is employed to obtain a model of the physician's visual behavior, which allows for the sampling of voxel intensity features that are then fed to an SVM classifier. This approach is compared with commonly used automatic feature selection alternatives. Image data were taken from the Alzheimer's Disease Neuroimaging Initiative database. The results show that the proposed approach marginally improves accuracy in AD vs. CN classification, but for MCI vs. CN and AD vs. MCI it presents lower performance.

Published in:

2012 9th IEEE International Symposium on Biomedical Imaging (ISBI)

Date of Conference:

2-5 May 2012