Group Sparse Representation for Prediction of MCI Conversion to AD

Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9226)

Abstract

Early Diagnose of Alzheimer’s disease (AD) is a problem which scientists are committed to solving for a long time. As the prodromal stage of AD, the mild cognitive impairment (MCI) patients have high risk of conversion to AD. Thus, for early diagnosis and possible early treatment of AD, it is important for accurate prediction of MCI conversion to AD, i.e., classification between MCI non-converter (MCI-NC) and MCI converter (MCI-C). In this paper, we propose a group discriminative sparse representation algorithm for prediction of MCI conversion to AD. Unlike the previous researches which are based on l1-norm sparse representation classification (SRC), we focus on how to mining the group label information which will help us to do the classification more correctly and efficiently. We apply the group label restricted condition as well as the sparse condition when doing the sparse coding procedure, which makes the sparse coding coefficients discriminative. The Moreau-Yosida regularization method is utilized to help us solving this convex optimization problem. In our experiments on magnetic resonance brain images of 403 MCI patients (167 MCI-C and 236MCI-NC) from ADNI database, we demonstrate that the proposed method performs better than the traditional classification methods such as SRC with l1-norm and group sparse representation with l2-norm.

Keywords

AD/MCI diagnosis Group sparse representation Sparse representation classification 
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Notes

Acknowledgment

This work was supported by National Natural Science Foundation of China grants (No. 61005024, No. 61375112) and Medical and Engineering Foundation of Shanghai Jiao Tong University grants (No. YG2012MS12, No. YG2014MS39).

References

  1. 1.
    McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., Stadlan, E.M.: Clinical diagnosis of Alzheimer’s disease report of the NINCDS-ADRDA work group* under the auspices of department of health and human services task force on Alzheimer’s disease. Neurology 34(7), 939 (1984)CrossRefGoogle Scholar
  2. 2.
    Dubois, B., Feldman, H., Jacova, C., DeKosky, S.T., Barberger-Gateau, P., Cummings, J.: Research criteria for the diagnosis of Alzheimer’s disease: revising the NINCDS–ADRDA criteria. Lancet Neurol. 6(8), 734–746 (2007)CrossRefGoogle Scholar
  3. 3.
    Davatzikos, C., Bhatt, P., Shaw, L.M., Batmanghelich, K.N., Trojanowski, J.Q.: Prediction of MCI to AD conversion, via MRI, CSF biomarkers, and pattern classification. Neurobiol. Aging 32(12), 2322-e19 (2011)CrossRefGoogle Scholar
  4. 4.
    Risacher, S.L., Saykin, A.J., West, J.D., Shen, L., Firpi, H.A., McDonald, B.C.: Alzheimer’s disease neuroimaging initiative (ADNI): baseline MRI predictors of conversion from MCI to probable AD in the ADNI cohort. Curr. Alzheimer Res. 6(4), 347 (2009)CrossRefGoogle Scholar
  5. 5.
    Cheng, B., Liu, M., Suk, H.I., Shen, D., Zhang, D.: Alzheimer’s disease neuroimaging initiative: multimodal manifold-regularized transfer learning for MCI conversion prediction. Brain Imaging Behav. 1–14 (2015)Google Scholar
  6. 6.
    Wright, J., Yang, A.Y., Ganesh, A., Sastry, S.S., Ma, Y.: Robust face recognition via sparse representation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 210–227 (2009)CrossRefMATHGoogle Scholar
  7. 7.
    Lee, H., Battle, A., Raina, R., Ng, A.Y.: Efficient sparse coding algorithms. In: Advances in Neural Information Processing Systems, pp. 801–808 (2006)Google Scholar
  8. 8.
    Mallat, S.G., Zhang, Z.: Matching pursuits with time-frequency dictionaries. IEEE Trans. Sig. Process. 41(12), 3397–3415 (1993)CrossRefGoogle Scholar
  9. 9.
    Pati, Y.C., Rezaiifar, R., Krishnaprasad, P.S.: Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition. In: 1993 Conference Record of the Twenty-Seventh Asilomar Conference on Signals, Systems and Computers, pp. 40–44. IEEE, November 1993Google Scholar
  10. 10.
    Needell, D., Vershynin, R.: Uniform uncertainty principle and signal recovery via regularized orthogonal matching pursuit. Found. Comput. Math. 9(3), 317–334 (2009)MathSciNetCrossRefMATHGoogle Scholar
  11. 11.
    Elad, M., Aharon, M.: Image denoising via sparse and redundant representations over learned dictionaries. IEEE Trans. Image Process. 15(12), 3736–3745 (2006)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Protter, M., Elad, M.: Image sequence denoising via sparse and redundant representations. IEEE Trans. Image Process. 18(1), 27–35 (2009)MathSciNetCrossRefMATHGoogle Scholar
  13. 13.
    Dong, W., Zhang, D., Shi, G.: Centralized sparse representation for image restoration. In: 2011 IEEE International Conference on Computer Vision (ICCV), pp. 1259–1266. IEEE, November 2011Google Scholar
  14. 14.
    Yang, J., Wright, J., Huang, T.S., Ma, Y.: Image super-resolution via sparse representation. IEEE Trans. Image Process. 19(11), 2861–2873 (2010)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)CrossRefGoogle Scholar
  16. 16.
    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
  17. 17.
    Wang, Y., Nie, J., Yap, P.-T., Shi, F., Guo, L., Shen, D.: Robust deformable-surface-based skull-stripping for large-scale studies. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 635–642. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  18. 18.
    Shen, D., Davatzikos, C.: Very high-resolution morphometry using mass-preserving deformations and HAMMER elastic registration. NeuroImage 18(1), 28–41 (2003)CrossRefGoogle Scholar
  19. 19.
    Thompson, P.M., Schwartz, C., Toga, A.W.: High-resolution random mesh algorithms for creating a probabilistic 3D surface atlas of the human brain. NeuroImage 3(1), 19–34 (1996)CrossRefGoogle Scholar
  20. 20.
    Koh, K., Kim, S.J., Boyd, S.P.: An interior-point method for large-scale l1-regularized logistic regression. J. Mach. Learn. Res. 8(8), 1519–1555 (2007)MathSciNetGoogle Scholar
  21. 21.
    Liu, J., Ji, S., Ye, J.: SLEP: Sparse Learning with Efficient Projections, vol. 6, p. 491. Arizona State University, Arizona (2009)Google Scholar
  22. 22.
    Chi, Y.T., Ali, M., Rajwade, A., Ho, J.: Block and group regularized sparse modeling for dictionary learning. In: 2013 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 377–382. IEEE, June 2013Google Scholar
  23. 23.
    Liu, J., Ye, J.: Moreau-Yosida regularization for grouped tree structure learning. In: Advances in Neural Information Processing Systems, pp. 1459–1467 (2010)Google Scholar
  24. 24.
    Cheng, B., Zhang, D., Shen, D.: Domain transfer learning for MCI conversion prediction. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part I. LNCS, vol. 7510, pp. 82–90. Springer, Heidelberg (2012)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Instrument Science and Engineering, School of EIEEShanghai Jiao Tong UniversityShanghaiChina

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