Diagnosis of Alzheimer’s Disease Using View-Aligned Hypergraph Learning with Incomplete Multi-modality Data

  • Mingxia Liu
  • Jun Zhang
  • Pew-Thian Yap
  • Dinggang ShenEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9900)


Effectively utilizing incomplete multi-modality data for diagnosis of Alzheimer’s disease (AD) is still an area of active research. Several multi-view learning methods have recently been developed to deal with missing data, with each view corresponding to a specific modality or a combination of several modalities. However, existing methods usually ignore the underlying coherence among views, which may lead to sub-optimal learning performance. In this paper, we propose a view-aligned hypergraph learning (VAHL) method to explicitly model the coherence among the views. Specifically, we first divide the original data into several views based on possible combinations of modalities, followed by a sparse representation based hypergraph construction process in each view. A view-aligned hypergraph classification (VAHC) model is then proposed, by using a view-aligned regularizer to model the view coherence. We further assemble the class probability scores generated from VAHC via a multi-view label fusion method to make a final classification decision. We evaluate our method on the baseline ADNI-1 database having 807 subjects and three modalities (i.e., MRI, PET, and CSF). Our method achieves at least a \(4.6\,\%\) improvement in classification accuracy compared with state-of-the-art methods for AD/MCI diagnosis.


Positron Emission Tomography Mild Cognitive Impairment Singular Value Decomposition Sparse Representation Mild Cognitive Impairment Subject 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Brookmeyer, R., Johnson, E., Ziegler-Graham, K., Arrighi, H.M.: Forecasting the global burden of Alzheimer’s disease. Alzheimer’s Dement. 3(3), 186–191 (2007)CrossRefGoogle Scholar
  2. 2.
    Ingalhalikar, M., Parker, W.A., Bloy, L., Roberts, T.P.L., Verma, R.: Using multiparametric data with missing features for learning patterns of pathology. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012. LNCS, vol. 7512, pp. 468–475. Springer, Heidelberg (2012). doi: 10.1007/978-3-642-33454-2_58 CrossRefGoogle Scholar
  3. 3.
    Yuan, L., Wang, Y., Thompson, P.M., Narayan, V.A., Ye, J.: Multi-source feature learning for joint analysis of incomplete multiple heterogeneous neuroimaging data. NeuroImage 61(3), 622–632 (2012)CrossRefGoogle Scholar
  4. 4.
    Xiang, S., Yuan, L., Fan, W., Wang, Y., Thompson, P.M., Ye, J.: Bi-level multi-source learning for heterogeneous block-wise missing data. NeuroImage 102, 192–206 (2014)CrossRefGoogle Scholar
  5. 5.
    Thung, K.H., Wee, C.Y., Yap, P.T., Shen, D.: Neurodegenerative disease diagnosis using incomplete multi-modality data via matrix shrinkage and completion. NeuroImage 91, 386–400 (2014)CrossRefGoogle Scholar
  6. 6.
    Schneider, T.: Analysis of incomplete climate data: estimation of mean values and covariance matrices and imputation of missing values. J. Clim. 14(5), 853–871 (2001)CrossRefGoogle Scholar
  7. 7.
    Golub, G.H., Reinsch, C.: Singular value decomposition and least squares solutions. Numer. Math. 14(5), 403–420 (1970)MathSciNetCrossRefzbMATHGoogle Scholar
  8. 8.
    Zhou, D., Huang, J., Schölkopf, B.: Learning with hypergraphs: Clustering, classification, and embedding. In: NIPS, pp. 1601–1608(2006)Google Scholar
  9. 9.
    Gao, Y., Wang, M., Tao, D., Ji, R., Dai, Q.: 3-D object retrieval and recognition with hypergraph analysis. IEEE Trans. Image Process. 21(9), 4290–4303 (2012)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Jack, C.R., Bernstein, M.A., Fox, N.C., Thompson, P., Alexander, G., Harvey, D., Borowski, B., Britson, P.J., Whitwell, L., Ward, C.: The Alzheimer’s disease neuroimaging initiative (ADNI): MRI methods. J. Magn. Reson. Imaging 27(4), 685–691 (2008)CrossRefGoogle Scholar
  11. 11.
    Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction of intensity nonuniformity in MRI data. IEEE Trans. Med. Imaging 17(1), 87–97 (1998)CrossRefGoogle Scholar
  12. 12.
    Jenkinson, M., Beckmann, C.F., Behrens, T.E., Woolrich, M.W., Smith, S.M.: FSL. NeuroImage 62(2), 782–790 (2012)CrossRefGoogle Scholar
  13. 13.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. 31(2), 210–227 (2009)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Mingxia Liu
    • 1
  • Jun Zhang
    • 1
  • Pew-Thian Yap
    • 1
  • Dinggang Shen
    • 1
    Email author
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillUSA

Personalised recommendations