PURPOSE: This article presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of Alzheimer's disease (AD). Two hundred and ten 18F-FDG PET images from the ADNI initiative [52 normal controls (NC), 114 mild cognitive impairment (MCI), and 53 AD subjects] are studied. METHODS: The proposed methodology is based on the selection of voxels of interest using the t-test and a posterior reduction of the feature dimension using factor analysis. Factor loadings are used as features for three different classifiers: Two multivariate Gaussian mixture model, with linear and quadratic discriminant function, and a support vector machine with linear kernel. RESULTS: An accuracy rate up to 95% when NC and AD are considered and an accuracy rate up to 88% and 86% for NC-MCI and NC-MCI,AD, respectively, are obtained using SVM with linear kernel. CONCLUSIONS: Results are compared to the voxel-as-features and a PCA- based approach and the proposed methodology achieves better classification performance.
PURPOSE: This article presents a computer-aided diagnosis technique for improving the accuracy of the early diagnosis of Alzheimer's disease (AD). Two hundred and ten 18F-FDG PET images from the ADNI initiative [52 normal controls (NC), 114 mild cognitive impairment (MCI), and 53 AD subjects] are studied. METHODS: The proposed methodology is based on the selection of voxels of interest using the t-test and a posterior reduction of the feature dimension using factor analysis. Factor loadings are used as features for three different classifiers: Two multivariate Gaussian mixture model, with linear and quadratic discriminant function, and a support vector machine with linear kernel. RESULTS: An accuracy rate up to 95% when NC and AD are considered and an accuracy rate up to 88% and 86% for NC-MCI and NC-MCI,AD, respectively, are obtained using SVM with linear kernel. CONCLUSIONS: Results are compared to the voxel-as-features and a PCA- based approach and the proposed methodology achieves better classification performance.
Authors: Jessica B S Langbaum; Kewei Chen; Wendy Lee; Cole Reschke; Dan Bandy; Adam S Fleisher; Gene E Alexander; Norman L Foster; Michael W Weiner; Robert A Koeppe; William J Jagust; Eric M Reiman Journal: Neuroimage Date: 2009-01-21 Impact factor: 6.556
Authors: Roger Higdon; Norman L Foster; Robert A Koeppe; Charles S DeCarli; William J Jagust; Christopher M Clark; Nancy R Barbas; Steven E Arnold; R Scott Turner; Judith L Heidebrink; Satoshi Minoshima Journal: Stat Med Date: 2004-01-30 Impact factor: 2.373
Authors: Michael W Weiner; Dallas P Veitch; Paul S Aisen; Laurel A Beckett; Nigel J Cairns; Robert C Green; Danielle Harvey; Clifford R Jack; William Jagust; Enchi Liu; John C Morris; Ronald C Petersen; Andrew J Saykin; Mark E Schmidt; Leslie Shaw; Judith A Siuciak; Holly Soares; Arthur W Toga; John Q Trojanowski Journal: Alzheimers Dement Date: 2011-11-02 Impact factor: 21.566
Authors: Michael W Weiner; Dallas P Veitch; Paul S Aisen; Laurel A Beckett; Nigel J Cairns; Jesse Cedarbaum; Robert C Green; Danielle Harvey; Clifford R Jack; William Jagust; Johan Luthman; John C Morris; Ronald C Petersen; Andrew J Saykin; Leslie Shaw; Li Shen; Adam Schwarz; Arthur W Toga; John Q Trojanowski Journal: Alzheimers Dement Date: 2015-06 Impact factor: 21.566
Authors: Katherine R Gray; Robin Wolz; Rolf A Heckemann; Paul Aljabar; Alexander Hammers; Daniel Rueckert Journal: Neuroimage Date: 2012-01-06 Impact factor: 6.556
Authors: Michael W Weiner; Dallas P Veitch; Paul S Aisen; Laurel A Beckett; Nigel J Cairns; Robert C Green; Danielle Harvey; Clifford R Jack; William Jagust; Enchi Liu; John C Morris; Ronald C Petersen; Andrew J Saykin; Mark E Schmidt; Leslie Shaw; Li Shen; Judith A Siuciak; Holly Soares; Arthur W Toga; John Q Trojanowski Journal: Alzheimers Dement Date: 2013-08-07 Impact factor: 21.566