Hierarchical fusion of features and classifier decisions for Alzheimer's disease diagnosis
Article first published online: 18 FEB 2013
Copyright © 2013 Wiley Periodicals, Inc.
Human Brain Mapping
Volume 35, Issue 4, pages 1305–1319, April 2014
How to Cite
Liu, M., Zhang, D., Shen, D. and the Alzheimer's Disease Neuroimaging Initiative (2014), Hierarchical fusion of features and classifier decisions for Alzheimer's disease diagnosis. Hum. Brain Mapp., 35: 1305–1319. doi: 10.1002/hbm.22254
- Issue published online: 20 MAR 2014
- Article first published online: 18 FEB 2013
- Manuscript Accepted: 17 DEC 2012
- Manuscript Revised: 13 NOV 2012
- Manuscript Received: 30 AUG 2012
- NIH. Grant Numbers: EB006733, EB008374, EB009634, AG041721
- NBRPC 973 Program. Grant Number: 2010CB732505
- NSFC. Grant Numbers: 61005024, 61075010, 81271540
- Medical and Engineering Foundation of Shanghai Jiao Tong University. Grant Number: YG2012MS12
- Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health). Grant Number: U01 AG024904
- National Institute on Aging
- the National Institute of Biomedical Imaging and Bioengineering
- through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb
- Eisai Global Clinical Development
- Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics
- Johnson and Johnson, Eli Lilly and Co
- Medpace, Inc.
- Merck and Co., Inc.
- Novartis AG, Pfizer Inc.
- F. Hoffman-La Roche, Schering-Plough, Synarc, Inc.
- Alzheimer's Association and Alzheimer's Drug Discovery Foundation
- U.S. Food and Drug Administration
- brain disease diagnosis;
- Alzheimer's disease;
- mild cognitive impairment (MCI);
- hierarchical classification;
- local patch;
- SVM classifier
Pattern classification methods have been widely investigated for analysis of brain images to assist the diagnosis of Alzheimer's disease (AD) and its early stage such as mild cognitive impairment (MCI). By considering the nature of pathological changes, a large number of features related to both local brain regions and interbrain regions can be extracted for classification. However, it is challenging to design a single global classifier to integrate all these features for effective classification, due to the issue of small sample size. To this end, we propose a hierarchical ensemble classification method to combine multilevel classifiers by gradually integrating a large number of features from both local brain regions and interbrain regions. Thus, the large-scale classification problem can be divided into a set of small-scale and easier-to-solve problems in a bottom-up and local-to-global fashion, for more accurate classification. To demonstrate its performance, we use the spatially normalized grey matter (GM) of each MR brain image as imaging features. Specifically, we first partition the whole brain image into a number of local brain regions and, for each brain region, we build two low-level classifiers to transform local imaging features and the inter-region correlations into high-level features. Then, we generate multiple high-level classifiers, with each evaluating the high-level features from the respective brain regions. Finally, we combine the outputs of all high-level classifiers for making a final classification. Our method has been evaluated using the baseline MR images of 652 subjects (including 198 AD patients, 225 MCI patients, and 229 normal controls (NC)) from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The experimental results show that our classification method can achieve the accuracies of 92.0% and 85.3% for classifications of AD versus NC and MCI versus NC, respectively, demonstrating very promising classification performance compared to the state-of-the-art classification methods. Hum Brain Mapp 35:1305–1319, 2014. © 2013 Wiley Periodicals, Inc.