Article (Scientific journals)
Combining feature extraction methods to assist the diagnosis of Alzheimer's disease
Segovia, Fermin; Górriz, J. M.; Ramírez, J. et al.
2016In Current Alzheimer Research, 13
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Keywords :
Alzheimer disease; machine learning; feature exrtraction
Abstract :
[en] Neuroimaging data as 18F-FDG PET is widely used to assist the diagnosis of Alzheimer’s disease (AD). Looking for regions with hypoperfusion/ hypometabolism, clinicians may predict or corroborate the diagnosis of the patients. Modern computer aided diagnosis (CAD) systems based on the statistical analysis of whole neuroimages are more accurate than classical systems based on quantifying the uptake of some predefined regions of interests (ROIs). In addition, these new systems allow determining new ROIs and take advantage of the huge amount of information comprised in neuroimaging data. A major branch of modern CAD systems for AD is based on multivariate techniques, which analyse a neuroimage as a whole, considering not only the voxel intensities but also the relations among them. In order to deal with the vast dimensionality of the data, a number of feature extraction methods have been successfully applied. In this work, we propose a CAD system based on the combination of several feature extraction techniques. First, some commonly used feature extraction methods based on the analysis of the variance (as principal component analysis), on the factorization of the data (as non-negative matrix factorization) and on classical magnitudes (as Haralick features) were simultaneously applied to the original data. These feature sets were then combined by means of two different combination approaches: i) using a single classifier and a multiple kernel learning approach and ii) using an ensemble of classifier and selecting the final decision by majority voting. The proposed approach was evaluated using a labelled neuroimaging database along with a cross validation scheme. As conclusion, the proposed CAD system performed better than approaches using only one feature extraction technique. We also provide a fair comparison (using the same database).
Disciplines :
Engineering, computing & technology: Multidisciplinary, general & others
Neurology
Author, co-author :
Segovia, Fermin
Górriz, J. M.
Ramírez, J.
Phillips, Christophe   ;  Université de Liège > Centre de recherches du cyclotron
Alzheimer’s Disease Neuroimaging Initiative 
 These authors have contributed equally to this work.
Language :
English
Title :
Combining feature extraction methods to assist the diagnosis of Alzheimer's disease
Publication date :
2016
Journal title :
Current Alzheimer Research
ISSN :
1567-2050
Publisher :
Bentham Science Publishers Ltd., Amsterdam, Netherlands
Volume :
13
Peer reviewed :
Peer Reviewed verified by ORBi
Available on ORBi :
since 23 November 2015

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