Relationship induced multi-atlas learning for Alzheimer’s disease diagnosis

Mingxia Liu, Daoqiang Zhang, Ehsan Adeli-Mosabbeb, Dinggang Shen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)


Multi-atlas based methods using magnetic resonance imaging (MRI) have been recently proposed for automatic diagnosis of Alzheimer’s disease (AD) and its prodromal stage, i.e., mild cognitive impairment (MCI). However, most existing multi-atlas based methods simply average or concatenate features generated from multiple atlases, which ignores the important underlying structure information of multiatlas data. In this paper, we propose a novel relationship induced multiatlas learning (RIML) method for AD/MCI classification. Specifically, we first register each brain image onto multiple selected atlases separately, through which multiple sets of feature representations can be extracted. To exploit the structure information of data, we develop a relationship induced sparse feature selection method, by employing two regularization terms to model the relationships among atlases and among subjects. Finally, we learn a classifier based on selected features in each atlas space, followed by an ensemble classification strategy to combine multiple classifiers for making a final decision. Experimental results on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database demonstrate that our method achieves significant performance improvement for AD/MCI classification, compared with several state-of-the-art methods.

Original languageEnglish
Title of host publicationMedical Computer Vision
Subtitle of host publicationAlgorithms for Big Data - International Workshop, MCV 2015 and Held in Conjunction with MICCAI 2015, Revised Selected Papers
EditorsMichael Kelm, Henning Müller, Bjoern Menze, Shaoting Zhang, Dimitris Metaxas, Georg Langs, Albert Montillo, Weidong Cai
PublisherSpringer Verlag
Number of pages10
ISBN (Print)9783319420158
Publication statusPublished - 2016
Externally publishedYes
EventInternational Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI - Germany, Germany
Duration: 2015 Oct 92015 Oct 9

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9601 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


OtherInternational Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI

Bibliographical note

Funding Information:
This study was supported by NIH grants (EB006733, EB008374, EB009634, MH100217, AG041721, and AG042599), the National Natural Science Foundation of China (Nos. 61422204, 61473149), the Jiangsu Natural Science Foundation for Distinguished Young Scholar (No. BK20130034), and the NUAA Fundamental Research Fund under grant number NE2013105.

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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