Reference : Combining feature extraction methods to assist the diagnosis of Alzheimer's disease
Scientific journals : Article
Engineering, computing & technology : Multidisciplinary, general & others
Human health sciences : Neurology
Combining feature extraction methods to assist the diagnosis of Alzheimer's disease
Segovia, Fermin mailto [> >]
Górriz, J. M. [> >]
Ramírez, J. [> >]
Phillips, Christophe* mailto [Université de Liège > > Centre de recherches du cyclotron >]
Alzheimer’s Disease Neuroimaging Initiative, * [> >]
* These authors have contributed equally to this work.
Current Alzheimer Research
Bentham Science Publishers Ltd.
Yes (verified by ORBi)
The Netherlands
[en] Alzheimer disease ; machine learning ; feature exrtraction
[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).
Researchers ; Students ; General public

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