期刊
NEUROIMAGE
卷 47, 期 4, 页码 1476-1486出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2009.05.036
关键词
Alzheimer's disease; MCI; Hippocampus; Magnetic resonance imaging; Support vector machines
资金
- Alzheimer's Disease Neuroimaging Initiative (ADNI) [U01 AG024904]
- National Institute on Aging
- National Institute of Biomedical Imaging and Bioengineering (NIBIB)
- Pfizer Inc.
- Wyeth Research
- Bristol-Myers Squibb
- Eli Lilly and Company
- GlaxoSmithKline
- Merck Co. Inc.
- AstraZeneca AB
- Novartis Pharmaceuticals Corporation
- Alzheimer's Association
- Eisai Global Clinical Development
- Elan Corporation plc
- Forest Laboratories
- Institute for the Study of Aging
- U.S. Food and Drug Administration
We describe a new method to automatically discriminate between patients with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and elderly controls, based on multidimensional classification of hippocampal shape features. This approach uses spherical harmonics (SPHARM) coefficients to model the shape of the hippocampi, which are segmented from magnetic resonance images (MRI) using a fully automatic method that we previously developed. SPHARM coefficients are used as features in a classification procedure based on support vector machines (SVM). The most relevant features for classification are selected using a bagging strategy. We evaluate the accuracy of our method in a group of 23 patients with AD (10 males, 13 females, age standard-deviation (SD) = 73 +/- 6 years, mini-mental score (MMS) = 24.4 +/- 2.8). 23 patients with amnestic MCI (10 males, 13 females, age SD = 74 +/- 8 years, MMS = 27.3 +/- 1.4) and 25 elderly healthy controls (13 males, 12 females, age SD = 64 8 years), using leave-one-out cross-validation. For AD vs controls, we obtain a correct classification rate of 94%, a sensitivity of 96%, and a specificity of 92%. For MCI vs controls, we obtain a classification rate of 83%, a sensitivity of 83%, and a specificity of 84%. This accuracy is superior to that of hippocampal volumetry and is comparable to recently published SVM-based whole-brain classification methods, which relied on a different strategy. This new method may become a useful tool to assist in the diagnosis of Alzheimer's disease. (C) 2009 Elsevier Inc. All rights reserved.
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