Metric Space Structures for Computational Anatomy
This paper describes a method based on metric structures for anatomical analysis on a large set of brain MR images. A geodesic distance between each pair was measured using large deformation diffeomorphic metric mapping (LDDMM). Manifold learning approaches were applied to seek a low-dimensional embedding in the high- dimensional shape space, in which inference between healthy control and disease groups can be done using standard classification algorithms. In particular, the proposed method was evaluated on ADNI, a dataset for Alzheimer’s disease study. Our work demonstrates that the high-dimensional anatomical shape space of the amygdala and hippocampi can be approximated by a relatively low dimension manifold.
Keywordsstructural MR image computational anatomy Alzheimer’s disease manifold learning shape analysis
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- 4.Park, H.: Isomap induced manifold embedding and its application to Alzheimer’s disease and mild cognitive impairment. Neuroscience Letters (2012)Google Scholar
- 6.Gray, K., Aljabar, P., Heckemann, R., Hammers, A., Rueckert, D.: Random forest-based similarity measures for multi-modal classification of Alzheimer’s disease. NeuroImage (2012)Google Scholar
- 7.Wolz, R., Aljabar, P., Hajnal, J., Lötjönen, J., Rueckert, D.: Nonlinear dimensionality reduction combining mr imaging with non-imaging information. Medical Image Analysis (2011)Google Scholar
- 10.Miller, M., Priebe, C., Qiu, A., Fischl, B., Kolasny, A., Brown, T., Park, Y., Ratnanather, J., Busa, E., Jovicich, J., et al.: Collaborative computational anatomy: an mri morphometry study of the human brain via diffeomorphic metric mapping. Human Brain Mapping 30(7), 2132–2141 (2008)CrossRefGoogle Scholar
- 18.Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering. In: Advances in Neural Information Processing Systems, vol. 14, pp. 585–591 (2001)Google Scholar
- 19.Chang, C.C., Lin, C.J.: LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology 2, 27:1–27:27 (2011), Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm
- 20.Liaw, A., Wiener, M.: Classification and regression by randomforest. R News 2(3), 18–22 (2002)Google Scholar
- 23.Wang, L., Beg, F., Ratnanather, T., Ceritoglu, C., Younes, L., Morris, J.C., Csernansky, J.G., Miller, M.I.: Large deformation diffeomorphism and momentum based hippocampal shape discrimination in dementia of the Alzheimer type. IEEE Transactions on Medical Imaging 26(4), 462–470 (2007)CrossRefGoogle Scholar