Multi-modality Canonical Feature Selection for Alzheimer’s Disease Diagnosis

  • Xiaofeng Zhu
  • Heung-Il Suk
  • Dinggang Shen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8674)


Feature selection has been commonly regarded as an effective method to lessen the problem of high dimension and low sample size in medical image analysis. In this paper, we propose a novel multi-modality canonical feature selection method. Unlike the conventional sparse Multi-Task Learning (MTL) based feature selection method that mostly considered only the relationship between target response variables, we further consider the correlations between features of different modalities by projecting them into a canonical space determined by canonical correlation analysis. We call the projections as canonical representations. By setting the canonical representations as regressors in a sparse least square regression framework and by further penalizing the objective function with a new canonical regularizer on the weight coefficient matrix, we formulate a multi-modality canonical feature selection method. With the help of the canonical information of canonical representations and also a canonical regularizer, the proposed method selects canonical-cross-modality features that are useful for the tasks of clinical scores regression and multi-class disease identification. In our experiments on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, we combine Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) images to jointly predict clinical scores of Alzheimer’s Disease Assessment Scale-Cognitive subscale (ADAS-Cog) and Mini-Mental State Examination (MMSE) and also identify multi-class disease status for Alzheimer’s disease diagnosis.


Positron Emission Tomography Feature Selection Positron Emission Tomography Image Canonical Correlation Analysis Feature Selection Method 
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  1. 1.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern classification. John Wiley & Sons (2012)Google Scholar
  2. 2.
    Hardoon, D.R., Szedmak, S., Shawe-Taylor, J.: Canonical correlation analysis: An overview with application to learning methods. Neural Computation 16(12), 2639–2664 (2004)CrossRefGoogle Scholar
  3. 3.
    Kakade, S.M., Foster, D.P.: Multi-view regression via canonical correlation analysis. In: Bshouty, N.H., Gentile, C. (eds.) COLT. LNCS (LNAI), vol. 4539, pp. 82–96. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  4. 4.
    Liu, J., Ji, S., Ye, J.: Multi-task feature learning via efficient l2, 1-norm minimization. In: UAI, pp. 339–348 (2009)Google Scholar
  5. 5.
    May, A., Ashburner, J., Büchel, C., McGonigle, D., Friston, K., Frackowiak, R., Goadsby, P.: Correlation between structural and functional changes in brain in an idiopathic headache syndrome. Nature Medicine 5(7), 836–838 (1999)CrossRefGoogle Scholar
  6. 6.
    Perrin, R.J., Fagan, A.M., Holtzman, D.M.: Multimodal techniques for diagnosis and prognosis of Alzheimer’s disease. Nature 461, 916–922 (2009)CrossRefGoogle Scholar
  7. 7.
    Suk, H.-I., Shen, D.: Deep learning-based feature representation for AD/MCI classification. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part II. LNCS, vol. 8150, pp. 583–590. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  8. 8.
    Wang, H., Nie, F., Huang, H., Risacher, S., Saykin, A.J., Shen, L.: Identifying AD-sensitive and cognition-relevant imaging biomarkers via joint classification and regression. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part III. LNCS, vol. 6893, pp. 115–123. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  9. 9.
    Westman, E., Muehlboeck, J.S., Simmons, A.: Combining MRI and CSF measures for classification of Alzheimer’s disease and prediction of mild cognitive impairment conversion. NeuroImage 62(1), 229–238 (2012)CrossRefGoogle Scholar
  10. 10.
    Zhang, D., Shen, D.: Multi-modal multi-task learning for joint prediction of multiple regression and classification variables in Alzheimer’s disease. NeuroImage 59(2), 895–907 (2012)CrossRefGoogle Scholar
  11. 11.
    Zhu, X., Huang, Z., Shen, H.T., Cheng, J., Xu, C.: Dimensionality reduction by mixed kernel canonical correlation analysis. Pattern Recognition 45(8), 3003–3016 (2012)CrossRefzbMATHGoogle Scholar
  12. 12.
    Zhu, X., Huang, Z., Yang, Y., Shen, H.T., Xu, C., Luo, J.: Self-taught dimensionality reduction on the high-dimensional small-sized data. Pattern Recognition 46(1), 215–229 (2013)CrossRefGoogle Scholar
  13. 13.
    Zhu, X., Suk, H.I., Shen, D.: Matrix-similarity based loss function and feature selection for Alzheimer’s disease diagnosis. In: CVPR (2014)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Xiaofeng Zhu
    • 1
  • Heung-Il Suk
    • 1
  • Dinggang Shen
    • 1
  1. 1.Department of Radiology and BRICUniversity of North Carolina at Chapel HillUSA

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