Abstract
In order to establish the correspondences between different brains for comparison, spatial normalization based morphometric measurements have been widely used in the analysis of Alzheimer’s disease (AD). In the literature, different subjects are often compared in one atlas space, which may be insufficient in revealing complex brain changes. In this paper, instead of deploying one atlas for feature extraction and classification, we propose a maximum-margin based representation learning (MMRL) method to learn the optimal representation from multiple atlases. Unlike traditional methods that perform the representation learning separately from the classification, we propose to learn the new representation jointly with the classification model, which is more powerful in discriminating AD patients from normal controls (NC). We evaluated the proposed method on the ADNI database, and achieved 90.69% for AD/NC classification and 73.69% for p-MCI/s-MCI classification.
Chapter PDF
Similar content being viewed by others
Keywords
- Support Vector Machine
- Feature Selection
- Optimal Representation
- Registration Error
- Multiple Kernel Learning
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.
References
Ashburner, J., Friston, K.J.: Voxel-based morphometry – the methods. NeuroImage 11(6), 805–821 (2000)
Cuingnet, R., Gerardin, E., Tessieras, J., Auzias, G., Lehéricy, S., Habert, M.O., Chupin, M., Benali, H., Colliot, O.: 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)
Zhang, D., Wang, Y., Zhou, L., Yuan, H., Shen, D.: Multimodal classification of alzheimer’s disease / mild cognitive impairment. NeuroImage 55(3), 856–867 (2011)
Sabuncu, M.R., Balci, S.K., Shenton, M.E., Golland, P.: Image-driven population analysis through mixture modeling. IEEE TMI 28(9), 1473–1487 (2009)
Leporé, N., Brun, C.A., Pennec, X., Chou, Y.-Y., Lopez, O.L., Aizenstein, H.J., Becker, J.T., Toga, A.W., Thompson, P.M.: Mean template for tensor-based morphometry using deformation tensors. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part II. LNCS, vol. 4792, pp. 826–833. Springer, Heidelberg (2007)
Leporé, N., Brun, C., Chou, Y.Y., Lee, A., Barysheva, M., De Zubicaray, G.I., Meredith, M., Macmahon, K., Wright, M., Toga, A.W., Thompson, P.M.: Multi-atlas tensor-based morphometry and its application to a genetic study of 92 twins. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) 2nd Workshop on MFCA, MICCAI 2008. LNCS, pp. 48–55. Springer (2008)
Koikkalainen, J., Lötjönen, J., Thurfjell, L., Rueckert, D., Waldemar, G., Soininen, H.: Multi-template tensor-based morphometry: Application to analysis of alzheimer’s disease. NeuroImage 56(3), 1134–1144 (2011)
Frey, B.J., Dueck, D.: Clustering by passing messages between data points. Science 315(5814), 972–976 (2007)
Fan, Y., Shen, D., Gur, R.C., Gur, R.E., Davatzikos, C.: Compare: Classification of morphological patterns using adaptive regional elements. IEEE Trans. Med. Imaging 26(1), 93–105 (2007)
Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)
He, X., Cai, D., Niyogi, P.: Laplacian score for feature selection. In: NIPS, pp. 507–514. MIT Press, Cambridge (2006)
Bengio, Y.: Learning deep architectures for ai. Found. Trends Mach. Learn. 2(1), 1–127 (2009)
Peng, H., Long, F., Ding, C.: Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans. PAMI 27(8), 1226–1238 (2005)
Rakotomamonjy, A., Bach, F., Canu, S., Grandvalet, Y.: Simplemkl. Journal of Machine Learning Research 9 (November 2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Min, R., Cheng, J., Price, T., Wu, G., Shen, D. (2014). Maximum-Margin Based Representation Learning from Multiple Atlases for Alzheimer’s Disease Classification. In: Golland, P., Hata, N., Barillot, C., Hornegger, J., Howe, R. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. MICCAI 2014. Lecture Notes in Computer Science, vol 8674. Springer, Cham. https://doi.org/10.1007/978-3-319-10470-6_27
Download citation
DOI: https://doi.org/10.1007/978-3-319-10470-6_27
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10469-0
Online ISBN: 978-3-319-10470-6
eBook Packages: Computer ScienceComputer Science (R0)