NDLI: Hierarchical Fusion of Features and Classifier Decisions for Alzheimer’s Disease Diagnosis

Content Provider	PubMed Central
Author	Liu, Manhua Zhang, Daoqiang Shen, Dinggang
Copyright Year	2013
Abstract	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.
Related Links	http://dx.doi.org/10.1002/hbm.22254
Ending Page	1319
Page Count	15
Starting Page	1305
File Format	PDF
ISSN	10659471
e-ISSN	10970193
Journal	Human brain mapping
Issue Number	4
Volume Number	35
Language	English
Publisher Date	2014-04-01
Access Restriction	Open
Subject Keyword	Anatomy Radiological and Ultrasound Technology Radiology Nuclear Medicine and imaging Neurology Clinical Neurology Research in Higher Education
Content Type	Text
Resource Type	Article
Subject	Anatomy Neurology Radiology, Nuclear Medicine and Imaging Neurology (clinical) Radiological and Ultrasound Technology

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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