Abstract:
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.
journal_name
Med Physjournal_title
Medical physicsauthors
Salas-Gonzalez D,Górriz JM,Ramírez J,Illán IA,López M,Segovia F,Chaves R,Padilla P,Puntonet CGdoi
10.1118/1.3488894subject
Has Abstractpub_date
2010-11-01 00:00:00pages
6084-95issue
11eissn
0094-2405issn
2473-4209journal_volume
37pub_type
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