title: Multi-atlas based representations for Alzheimer's disease diagnosis.
abstract: Brain morphometry based classification from magnetic resonance (MR) acquisitions has been widely investigated in the diagnosis of Alzheimer's disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). In the literature, a morphometric representation of brain structures is obtained by spatial normalization of each image into a common space (i.e., a pre-defined atlas) via non-linear registration, thus the corresponding regions in different brains can be compared. However, representations generated from one single atlas may not be sufficient to reveal the underlying anatomical differences between the groups of disease-affected patients and normal controls (NC). In this article, we propose a different methodology, namely the multi-atlas based morphometry, which measures morphometric representations of the same image in different spaces of multiple atlases. Representations generated from different atlases can thus provide the complementary information to discriminate different groups, and also reduce the negative impacts from registration errors. Specifically, each studied subject is registered to multiple atlases, where adaptive regional features are extracted. Then, all features from different atlases are jointly selected by a correlation and relevance based scheme, followed by final classification with the support vector machine (SVM). We have evaluated the proposed method on 459 subjects (97 AD, 117 progressive-MCI (p-MCI), 117 stable-MCI (s-MCI), and 128 NC) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, and achieved 91.64% for AD/NC classification and 72.41% for p-MCI/s-MCI classification. Our results clearly demonstrate that the proposed multi-atlas based method can significantly outperform the previous single-atlas based methods.
authors.affiliation: Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina.
authors.forename: Rui
authors.initials: R
authors.lastname: Min
authors.forename: Guorong
authors.initials: G
authors.lastname: Wu
authors.forename: Jian
authors.initials: J
authors.lastname: Cheng
authors.forename: Qian
authors.initials: Q
authors.lastname: Wang
authors.forename: Dinggang
authors.initials: D
authors.lastname: Shen
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000175:diagnosis*; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000145:classification; D060825:Cognitive Dysfunction / Q000145:classification / Q000473:pathology; D016208:Databases, Factual / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D012680:Sensitivity and Specificity; D060388:Support Vector Machine
keywords: AD diagnosis; brain classification; multi-atlas based morphometry
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