Random Forest-Based Manifold Learning for Classification of Imaging Data in Dementia

  • Katherine R. Gray
  • Paul Aljabar
  • Rolf A. Heckemann
  • Alexander Hammers
  • Daniel Rueckert
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)

Abstract

Neurodegenerative disorders are characterized by changes in multiple biomarkers, which may provide complementary information for diagnosis and prognosis. We present a framework in which proximities derived from random forests are used to learn a low-dimensional manifold from labelled training data and then to infer the clinical labels of test data mapped to this space. The proposed method facilitates the combination of embeddings from multiple datasets, resulting in the generation of a joint embedding that simultaneously encodes information about all the available features. It is possible to combine different types of data without additional processing, and we demonstrate this key feature by application to voxel-based FDG-PET and region-based MR imaging data from the ADNI study. Classification based on the joint embedding coordinates out-performs classification based on either modality alone. Results are impressive compared with other state-of-the-art machine learning techniques applied to multi-modality imaging data.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15(6), 1373–1396 (2003)CrossRefMATHGoogle Scholar
  2. 2.
    Aljabar, P., Rueckert, D., Crum, W.: Automated morphological analysis of magnetic resonance brain imaging using spectral analysis. Neuroimage 43(2), 225–235 (2008)CrossRefGoogle Scholar
  3. 3.
    Wachinger, C., Yigitsoy, M., Navab, N.: Manifold learning for image-based breathing gating with application to 4D ultrasound. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6362, pp. 26–33. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  4. 4.
    Tenenbaum, J., de Silva, V., Langford, J.: A global geometric framework for non-linear dimensionality reduction. Science 290(5500), 2319–2323 (2000)CrossRefGoogle Scholar
  5. 5.
    Gerber, S., Tasdizen, T., Joshi, S., Whitaker, R.: On the manifold structure of the space of brain images. In: Yang, G.-Z., Hawkes, D., Rueckert, D., Noble, A., Taylor, C. (eds.) MICCAI 2009. LNCS, vol. 5761, pp. 305–312. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Aljabar, P., Wolz, R., Srinivasan, L., Counsell, S., Boardman, J.P., Murgasova, M., Doria, V., Rutherford, M.A., Edwards, A.D., Hajnal, J.V., Rueckert, D.: Combining morphological information in a manifold learning framework: Application to neonatal MRI. In: Jiang, T., Navab, N., Pluim, J.P.W., Viergever, M.A. (eds.) MICCAI 2010. LNCS, vol. 6363, pp. 1–8. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  7. 7.
    Breiman, L.: Random forests. Machine Learning 45(1), 5–32 (2001)CrossRefMATHGoogle Scholar
  8. 8.
    Cox, T.F., Cox, M.A.A.: Multidimensional scaling. Chapman and Hall, Boca Raton (2001)MATHGoogle Scholar
  9. 9.
    Hastie, T., Tibshirani, R., Friedman, J.: The elements of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, Heidelberg (2011); corrected 5th printing MATHGoogle Scholar
  10. 10.
    Shi, T., Horvath, S.: Unsupervised learning with random forest predictors. J. Comp. Graph. Stat. 15(1), 118–138 (2006)MathSciNetCrossRefGoogle Scholar
  11. 11.
    Zhang, D., Wang, Y., Zhou, L., et al.: Multimodal classification of Alzheimer’s disease and mild cognitive impairment. Neuroimage 55(3), 856–867 (2011)CrossRefGoogle Scholar
  12. 12.
    Hinrichs, C., Singh, V., Xu, G., et al.: Predictive markers for AD in a multi-modality framework: an analysis of MCI progression in the ADNI population. Neuroimage 55(2), 574–589 (2011)CrossRefGoogle Scholar
  13. 13.
    Heckemann, R.A., Keihaninejad, S., Aljabar, P., et al.: Improving intersubject image registration using tissue-class information benefits robustness and accuracy of multi-atlas based anatomical segmentation. Neuroimage 51(1), 221–227 (2010)CrossRefGoogle Scholar
  14. 14.
    Heckemann, R.A., Keihaninejad, S., Aljabar, P., et al.: Automatic morphometry in Alzheimer’s disease and mild cognitive impairment. Neuroimage 56(4), 2024–2037 (2011)CrossRefGoogle Scholar
  15. 15.
    Clarkson, M.J., Ourselin, S., Nielsen, C., et al.: Comparison of phantom and registration scaling corrections using the ADNI cohort. Neuroimage 47(4), 1506–1513 (2009)CrossRefGoogle Scholar
  16. 16.
    Joshi, A., Koeppe, R.A., Fessler, J.A.: Reducing between scanner differences in multi-center PET studies. Neuroimage 46(1), 154–159 (2009)CrossRefGoogle Scholar
  17. 17.
    Yakushev, I., Hammers, A., Fellgiebel, A., et al.: SPM-based count normalization provides excellent discrimination of mild Alzheimer’s disease and amnestic mild cognitive impairment from healthy aging. Neuroimage 44(1), 43–50 (2009)CrossRefGoogle Scholar
  18. 18.
    Breiman, L., Friedman, J.H., Olshen, R.A., et al.: Classification and regression trees. Wadsworth, Belmont (1984)MATHGoogle Scholar
  19. 19.
    Hampel, H., Burger, K., Teipel, S.J., et al.: Core candidate neurochemical and imaging biomarkers of Alzheimer’s disease. Alzh. & Dementia 4(1), 38–48 (2008)CrossRefGoogle Scholar
  20. 20.
    Patwardhan, M.B., McCrory, D.C., Matchar, D.B., et al.: Alzheimer disease: operating characteristics of PET – a meta-analysis. Radiology 231(1), 73–80 (2004)CrossRefGoogle Scholar
  21. 21.
    Cuingnet, R., Gerardin, E., Tessieras, J., et al.: Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 56(2), 766–781 (2011)CrossRefGoogle Scholar
  22. 22.
    Ranginwala, N.A., Hynan, L.S., Weiner, M.F., et al.: Clinical criteria for the diagnosis of Alzheimer disease: still good after all these years. Am. J. Geriat. Psychiatry 16(5), 384–388 (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Katherine R. Gray
    • 1
  • Paul Aljabar
    • 1
  • Rolf A. Heckemann
    • 2
    • 3
  • Alexander Hammers
    • 2
    • 3
  • Daniel Rueckert
    • 1
  1. 1.Department of ComputingImperial College LondonUnited Kingdom
  2. 2.Fondation Neurodis, CERMEP-Imagerie du VivantLyonFrance
  3. 3.Faculty of MedicineImperial College LondonUnited Kingdom

Personalised recommendations