NDLI: Automated classification of positron emission tomography images with mild cognitive impairment based on voxel of interest and neuropsychological test results

Content Provider	IEEE Xplore Digital Library
Author	Wenlu Yang Fangyu He
Copyright Year	2013
Description	Author affiliation: Coll. of Inf. Eng., Shanghai Maritime Univ., Shanghai, China (Wenlu Yang; Fangyu He)
Abstract	Alzheimer's disease (AD) is the most common form of dementia with the unknown pathogenesis and pathologies. It brings about serious social problems. As predementia AD, mild cognitive impairment (MCI) subjects are usually overlooked because of the cryptic features of the occurrence and development of the disease. To detect MCI subjects from healthy controls as early and accurately as possible is of great importance and urgency to delay or prevent the onset of AD. In the paper, we propose a novel systematic method combining voxel of interest in positron emission tomography (PET) images and neu-ropsychological test results for automated classification of MCI subjects versus healthy controls (HC) in the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The method includes four steps: pre-processing, extracting independent components, selecting voxels of interest, and classifying using a support vector machine classifier. PET images were obtained from ADNI database including 91 HC and 105 MCI patients with baseline diagnosis of MCI. As a result, we achieved good discrimination between MCI patients and HC with the averaged classification accuracy of 95.58%, sensitivity of 94.33%, and specificity of 97.04%. The experimental results show that the proposed method can successfully distinguish MCI from HC, and it is able to obtain higher classification accuracy of MCI versus HC than using only independent components or neuropsychological test results.
Sponsorship	IEEE Circuits Syst. Soc.
Starting Page	1042
Ending Page	1046
File Size	252510
Page Count	5
File Format	PDF
ISBN	9781467347143
ISSN	21579563
DOI	10.1109/ICNC.2013.6818130
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-07-23
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Support vector machines ADNI Accuracy voxels of interest support vector machine Magnetic resonance imaging Feature extraction mild cognitive impairment classification Positron emission tomography Alzheimer's disease
Content Type	Text
Resource Type	Article

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