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2015 | OriginalPaper | Buchkapitel

Group Testing for Longitudinal Data

verfasst von : Yi Hong, Nikhil Singh, Roland Kwitt, Marc Niethammer

Erschienen in: Information Processing in Medical Imaging

Verlag: Springer International Publishing

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Abstract

We consider how to test for group differences of shapes given longitudinal data. In particular, we are interested in differences of longitudinal models of each group’s subjects. We introduce a generalization of principal geodesic analysis to the tangent bundle of a shape space. This allows the estimation of the variance and principal directions of the distribution of trajectories that summarize shape variations within the longitudinal data. Each trajectory is parameterized as a point in the tangent bundle. To study statistical differences in two distributions of trajectories, we generalize the Bhattacharyya distance in Euclidean space to the tangent bundle. This not only allows to take second-order statistics into account, but also serves as our test-statistic during permutation testing. Our method is validated on both synthetic and real data, and the experimental results indicate improved statistical power in identifying group differences. In fact, our study sheds new light on group differences in longitudinal corpus callosum shapes of subjects with dementia versus normal controls.

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Fußnoten
1
A better estimate of the covariance matrix may be obtained, e.g., by using [8] or [2].
 
2
The threshold \(\epsilon \) varies with the application. In our experiments, we set it to 1e-6. Usually, the eigenvalues larger than \(\epsilon \) cover almost \(99\,\%\) of the variances.
 
3
We use two geodesics to connect three given shapes and uniformly sample points on these two geodesics. Then, by connecting opposing points, we obtain new geodesics which are located within the triangle region to sample a population of shapes.
 
4
The average of two generalized squared-Mahalanobis distances is related to the first term of the generalized Bhattacharyya distance in Eq. (4).
 
Literatur
1.
Zurück zum Zitat Bhattacharyya, A.: On a measure of divergence between two multinomial populations. Sankhyā Indian J. Stat. 7(4), 401–406 (1946)MATH Bhattacharyya, A.: On a measure of divergence between two multinomial populations. Sankhyā Indian J. Stat. 7(4), 401–406 (1946)MATH
3.
Zurück zum Zitat Durrleman, S., Pennec, X., Trouvé, A., Braga, J., Gerig, G., Ayache, N.: Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. IJCV 103(1), 22–59 (2013)MATHCrossRef Durrleman, S., Pennec, X., Trouvé, A., Braga, J., Gerig, G., Ayache, N.: Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. IJCV 103(1), 22–59 (2013)MATHCrossRef
4.
5.
Zurück zum Zitat Fletcher, P.T., Lu, C., Pizer, S.M., Joshi, S.: Principal geodesic analysis for the study of nonlinear statistics of shape. IEEE TMI 23(8), 995–1005 (2004) Fletcher, P.T., Lu, C., Pizer, S.M., Joshi, S.: Principal geodesic analysis for the study of nonlinear statistics of shape. IEEE TMI 23(8), 995–1005 (2004)
6.
Zurück zum Zitat Gilmore, J.H., Shi, F., Woolson, S.L., Knickmeyer, R.C., Short, S.J., Lin, W., Zhu, H., Hamer, R.M., Styner, M., Shen, D.: Longitudinal development of cortical and subcortical gray matter from birth to 2 years. Cereb. Cortex 22(11), 2478–2485 (2012)CrossRef Gilmore, J.H., Shi, F., Woolson, S.L., Knickmeyer, R.C., Short, S.J., Lin, W., Zhu, H., Hamer, R.M., Styner, M., Shen, D.: Longitudinal development of cortical and subcortical gray matter from birth to 2 years. Cereb. Cortex 22(11), 2478–2485 (2012)CrossRef
7.
Zurück zum Zitat Hong, Y., Joshi, S., Sanchez, M., Styner, M., Niethammer, M.: Metamorphic geodesic regression. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 197–205. Springer, Heidelberg (2012) CrossRef Hong, Y., Joshi, S., Sanchez, M., Styner, M., Niethammer, M.: Metamorphic geodesic regression. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds.) MICCAI 2012, Part III. LNCS, vol. 7512, pp. 197–205. Springer, Heidelberg (2012) CrossRef
8.
Zurück zum Zitat Ledoit, O., Wolf, M.: A well-conditioned estimator for large-dimensional covariance matrices. J. Multivar. Anal. 88(2), 365–411 (2004)MATHMathSciNetCrossRef Ledoit, O., Wolf, M.: A well-conditioned estimator for large-dimensional covariance matrices. J. Multivar. Anal. 88(2), 365–411 (2004)MATHMathSciNetCrossRef
9.
Zurück zum Zitat Lee, J.: Introduction to Smooth Manifolds. Springer, New York (2012) CrossRef Lee, J.: Introduction to Smooth Manifolds. Springer, New York (2012) CrossRef
10.
Zurück zum Zitat Mahalanobis, P.C.: On the generalized distance in statistics. Proc. Natl. Inst. Sci. (Calcutta) 2, 49–55 (1936)MATH Mahalanobis, P.C.: On the generalized distance in statistics. Proc. Natl. Inst. Sci. (Calcutta) 2, 49–55 (1936)MATH
11.
Zurück zum Zitat Marcus, D.S., Fotenos, A.F., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies: longitudinal mri data in nondemented and demented older adults. J. Cogn. Neurosci. 22(12), 2677–2684 (2010)CrossRef Marcus, D.S., Fotenos, A.F., Csernansky, J.G., Morris, J.C., Buckner, R.L.: Open access series of imaging studies: longitudinal mri data in nondemented and demented older adults. J. Cogn. Neurosci. 22(12), 2677–2684 (2010)CrossRef
12.
Zurück zum Zitat Muralidharan, P., Fletcher, P.T.: Sasaki metrics for analysis of longitudinal data on manifolds. In: CVPR, pp. 1027–1034 (2012) Muralidharan, P., Fletcher, P.T.: Sasaki metrics for analysis of longitudinal data on manifolds. In: CVPR, pp. 1027–1034 (2012)
13.
Zurück zum Zitat Niethammer, M., Huang, Y., Vialard, F.-X.: Geodesic regression for image time-series. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 655–662. Springer, Heidelberg (2011) CrossRef Niethammer, M., Huang, Y., Vialard, F.-X.: Geodesic regression for image time-series. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 655–662. Springer, Heidelberg (2011) CrossRef
15.
Zurück zum Zitat Sasaki, S.: On the differential geometry of tangent bundles of riemannian manifolds. TMJ 10(3), 338–354 (1958)MATH Sasaki, S.: On the differential geometry of tangent bundles of riemannian manifolds. TMJ 10(3), 338–354 (1958)MATH
16.
Zurück zum Zitat Singh, N., Hinkle, J., Joshi, S., Fletcher, P.T.: A hierarchical geodesic model for diffeomorphic longitudinal shape analysis. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 560–571. Springer, Heidelberg (2013) CrossRef Singh, N., Hinkle, J., Joshi, S., Fletcher, P.T.: A hierarchical geodesic model for diffeomorphic longitudinal shape analysis. In: Gee, J.C., Joshi, S., Pohl, K.M., Wells, W.M., Zöllei, L. (eds.) IPMI 2013. LNCS, vol. 7917, pp. 560–571. Springer, Heidelberg (2013) CrossRef
17.
Zurück zum Zitat Su, J., Kurtek, S., Klassen, E., Srivastava, A.: Statistical analysis of trajectories on riemannian manifolds: bird migration, hurricane tracking and video surveillance. Ann. Appl. Stat. 8(1), 530–552 (2014)MATHMathSciNetCrossRef Su, J., Kurtek, S., Klassen, E., Srivastava, A.: Statistical analysis of trajectories on riemannian manifolds: bird migration, hurricane tracking and video surveillance. Ann. Appl. Stat. 8(1), 530–552 (2014)MATHMathSciNetCrossRef
18.
Zurück zum Zitat Su, J., Srivastrava, A., de Souza, F., Sarkar, S.: Rate-invariant analysis of trajectories on riemannian manifolds with application in visual speech recognition. In: CVPR, pp. 620–627 (2014) Su, J., Srivastrava, A., de Souza, F., Sarkar, S.: Rate-invariant analysis of trajectories on riemannian manifolds with application in visual speech recognition. In: CVPR, pp. 620–627 (2014)
Metadaten
Titel
Group Testing for Longitudinal Data
verfasst von
Yi Hong
Nikhil Singh
Roland Kwitt
Marc Niethammer
Copyright-Jahr
2015
DOI
https://doi.org/10.1007/978-3-319-19992-4_11

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