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Multi-Atlas Based Representations for Alzheimer's Disease Diagnosis

Authors
Min, RuiWu, GuorongCheng, JianWang, QianShen, Dinggang
Issue Date
10월-2014
Publisher
WILEY
Keywords
multi-atlas based morphometry; AD diagnosis; brain classification
Citation
HUMAN BRAIN MAPPING, v.35, no.10, pp.5052 - 5070
Indexed
SCIE
SCOPUS
Journal Title
HUMAN BRAIN MAPPING
Volume
35
Number
10
Start Page
5052
End Page
5070
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/97154
DOI
10.1002/hbm.22531
ISSN
1065-9471
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. Hum Brain Mapp 35:5052-5070, 2014. (c) 2014 Wiley Periodicals, Inc.
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