View-centralized multi-atlas classification for Alzheimer's disease diagnosis

Alzheimer's Disease Neuroimaging Initiative

Research output: Contribution to journalArticle

50 Citations (Scopus)

Abstract

Multi-atlas based methods have been recently used for classification of Alzheimer's disease (AD) and its prodromal stage, that is, mild cognitive impairment (MCI). Compared with traditional single-atlas based methods, multiatlas based methods adopt multiple predefined atlases and thus are less biased by a certain atlas. However, most existing multiatlas based methods simply average or concatenate the features from multiple atlases, which may ignore the potentially important diagnosis information related to the anatomical differences among different atlases. In this paper, we propose a novel view (i.e., atlas) centralized multi-atlas classification method, which can better exploit useful information in multiple feature representations from different atlases. Specifically, all brain images are registered onto multiple atlases individually, to extract feature representations in each atlas space. Then, the proposed view-centralized multi-atlas feature selection method is used to select the most discriminative features from each atlas with extra guidance from other atlases. Next, we design a support vector machine (SVM) classifier using the selected features in each atlas space. Finally, we combine multiple SVM classifiers for multiple atlases through a classifier ensemble strategy for making a final decision. We have evaluated our method on 459 subjects [including 97 AD, 117 progressive MCI (p-MCI), 117 stable MCI (s-MCI), and 128 normal controls (NC)] from the Alzheimer's Disease Neuroimaging Initiative database, and achieved an accuracy of 92.51% for AD versus NC classification and an accuracy of 78.88% for p-MCI versus s-MCI classification. These results demonstrate that the proposed method can significantly outperform the previous multi-atlas based classification methods.

Original languageEnglish
Pages (from-to)1847-1865
Number of pages19
JournalHuman Brain Mapping
Volume36
Issue number5
DOIs
Publication statusPublished - 2015 May 1

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Atlases
Alzheimer Disease
Prodromal Symptoms

Keywords

  • Alzheimer's disease
  • Ensemble learning
  • Feature selection
  • Multiatlas classification
  • Multiview learning

ASJC Scopus subject areas

  • Clinical Neurology
  • Anatomy
  • Neurology
  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

Cite this

View-centralized multi-atlas classification for Alzheimer's disease diagnosis. / Alzheimer's Disease Neuroimaging Initiative.

In: Human Brain Mapping, Vol. 36, No. 5, 01.05.2015, p. 1847-1865.

Research output: Contribution to journalArticle

Alzheimer's Disease Neuroimaging Initiative 2015, 'View-centralized multi-atlas classification for Alzheimer's disease diagnosis', Human Brain Mapping, vol. 36, no. 5, pp. 1847-1865. https://doi.org/10.1002/hbm.22741
Alzheimer's Disease Neuroimaging Initiative. / View-centralized multi-atlas classification for Alzheimer's disease diagnosis. In: Human Brain Mapping. 2015 ; Vol. 36, No. 5. pp. 1847-1865.
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