NDLI: SVM feature selection for classification of SPECT images of Alzheimer's disease using spatial information

Content Provider	Springer Nature Link
Author	Fung, Glenn Stoeckel, Jonathan
Copyright Year	2006
Abstract	Alzheimer's disease is the most frequent type of dementia for elderly patients. Due to aging populations, the occurrence of this disease will increase in the next years. Early diagnosis is crucial to be able to develop more powerful treatments. Brain perfusion changes can be a marker for Alzheimer's disease. In this article, we study the use of SPECT perfusion imaging for the diagnosis of Alzheimer's disease differentiating between images from healthy subjects and images from Alzheimer's disease patients. Our classification approach is based on a linear programming formulation similar to the 1-norm support vector machines. In contrast with other linear hyperplane-based methods that perform simultaneous feature selection and classification, our proposed formulation incorporates proximity information about the features and generates a classifier that does not just select the most relevant voxels but the most relevant “areas” for classification resulting in more robust classifiers that are better suitable for interpretation. This approach is compared with the classical Fisher linear discriminant (FLD) classifier as well as with statistical parametric mapping (SPM). We tested our method on data from four European institutions. Our method achieved sensitivity of 84.4% at 90.9% specificity, this is considerable better the human experts. Our method also outperformed the FLD and SPM techniques. We conclude that our approach has the potential to be a useful help for clinicians.
Starting Page	243
Ending Page	258
Page Count	16
File Format	PDF
ISSN	02191377
Journal	Knowledge and Information Systems
Volume Number	11
Issue Number	2
e-ISSN	02193116
Language	English
Publisher	Springer-Verlag
Publisher Date	2006-09-21
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Alzheimer's disease Support vector machines Medical imaging Mathematical programming Business Information Systems Information Systems and Communication Service
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Information Systems Human-Computer Interaction Hardware and Architecture Software

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