NDLI: Computer aided diagnosis of the Alzheimer's disease combining SPECT-based feature selection and random forest classifiers

Content Provider	IEEE Xplore Digital Library
Author	Ramirez, J. Chaves, R. Gorriz, J.M. Lopez, M. Lvarez, I.A. Salas-Gonzalez, D. Segovia, F. Padilla, P.
Copyright Year	2009
Description	Author affiliation: Department of Signal Theory, Networking and Communications. University of Granada, Spain (Ramirez, J.; Chaves, R.; Gorriz, J.M.; Lopez, M.; Lvarez, I.A.; Salas-Gonzalez, D.; Segovia, F.; Padilla, P.)
Abstract	Alzheimer's disease (AD) is the most common cause of dementia in the elderly and affects approximately 30 million individuals worldwide. With the growth of the older population in developed nations, the prevalence of AD is expected to triple over the next 50 years while its early diagnosis remains being a difficult task. Functional imaging modalities including singlephoton emission computed tomography (SPECT) and positron emission tomography (PET) are often used with the aim of achieving early diagnosis. However, conventional evaluation of SPECT images often relies on manual reorientation, visual reading of tomographic slices and semiquantitative analysis of certain regions of interest (ROIs). These steps are time consuming, subjective and prone to error. This paper shows a computer aided diagnosis (CAD) technique for the early detection of the Alzheimer's disease (AD) based on SPECT image feature selection and a random forest classifier. The dimension of the voxel intensities feature space is reduced by defining normalized mean squared error (NMSE) features over regions of interest (ROI) that are selected by a t-test feature selection with feature correlation weighting. A random forest classifier is then trained based on a carefully prepared SPECT database in order to classify a given unknown patient record. The proposed method yields an up to 96% classification accuracy, thus outperforming recent developed methods for early AD diagnosis.
Starting Page	2738
Ending Page	2742
File Size	205822
Page Count	5
File Format	PDF
ISBN	9781424439614
ISSN	10957863
DOI	10.1109/NSSMIC.2009.5401968
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-10-24
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Alzheimer's disease Diversity reception Positron emission tomography Computer errors Dementia Senior citizens Computed tomography Image analysis Coronary arteriosclerosis Spatial databases
Content Type	Text
Resource Type	Article

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