Abstract
Nonrigid image registration methods based on the optimization of information-theoretic measures provide versatile solutions for robustly aligning mono-modal data with nonlinear variations and multi-modal data in radiology. Whereas mutual information and its variations arise as a first choice, generalized information measures offer relevant alternatives in specific clinical contexts. Their usual application setting is the alignement of image pairs by statistically matching scalar random variables (generally, greylevel distributions), handled via their probability densities. In this paper, we address the issue of estimating and optimizing generalized information measures over high-dimensional state spaces to derive multi-feature statistical nonrigid registration models. Specifically, we introduce novel consistent and asymptotically unbiaised k nearest neighbors estimators of α-informations, and study their variational optimization over finite and infinite dimensional smooth transform spaces. The resulting theoretical framework provides a well-posed and computationally efficient alternative to entropic graph techniques. Its performances are assessed on two cardiological applications: measuring myocardial deformations in tagged MRI, and compensating cardio-thoracic motions in perfusion MRI.
Chapter PDF
Similar content being viewed by others
Keywords
References
Ahmad, I., Lin, P.: A nonparametric estimation of the entropy for absolutely continuous distributions. IEEE Transactions on Information Theory 22(3), 372–375 (1976)
Arya, S., Mount, D.M., Netanyahu, N.S., Silverman, R., Wu, A.: An optimal algorithm for approximate nearest neighbor searching. Journal of the ACM 45(6), 891–923 (1998)
Boltz, S., Debreuve, E., Barlaud, M.: High-dimensional statistical measure for region-of-interest tracking. IEEE Transactions on Image Processing 18(6), 1266–1283 (2009)
Goria, M., Leonenko, N., Mergel, V., Novi Inverardi, P.L.: A new class of random vector entropy estimators and its applications in testing statistical hypotheses. Journal of Nonparametric Statistics 17(3), 277–297 (2005)
Hamrouni, S., Rougon, N., Prêteux, F.: Multi-feature information-theoretic image registration: application to groupwise registration of perfusion MRI exams. In: Proceedings IEEE International Symposium on Biomedical Imaging: Fron Nano to Macro, Chicago, IL (2011)
Hamrouni, S., Rougon, N., Prêteux, F.: Groupwise registration of cardiac perfusion MRI sequences using normalized mutual information in high dimension. In: Proceedings SPIE Medical Imaging 2011 - Image Processing, Orlando, FL, vol. 7962 (2011)
Leonenko, N., Pronzato, L., Savani, V.: A class of Rényi information estimators for multidimensional densities. Annals of Statistics 36(5), 2153–2182 (2008)
Maes, F., Vandermeulen, D., Suetens, P.: Medical image registration using mutual information. Proceedings of the IEEE 91(10), 1699–1722 (2003)
Neemuchwala, H.F., Hero, A.O.: Entropic graphs for registration. In: Multi-sensor Image Fusion and its Applications. Marcel Dekker, New York (2004)
Petitjean, C., Rougon, N., Prêteux, F.: Building and using a statistical 3D motion atlas for analyzing myocardial contraction in MRI. In: Proceedings SPIE Medical Imaging 2004 - Image Processing, San Diego, CA, vol. 5370, pp. 253–264 (2004)
Pluim, J.P.W., Maintz, J.B.A., Viergever, M.A.: f-information measures in medical image registration. IEEE Transactions on Medical Imaging 23(12), 1508–1516 (2004)
Rougon, N., Petitjean, C., Prêteux, F., Cluzel, P., Grenier, P.: A non-rigid registration approach for quantifying myocardial contraction in tagged MRI using generalized information measures. Medical Image Analysis 9(4), 353–375 (2005)
Staring, M., van der Heide, U.A., Klein, S., Viergever, M.A., Pluim, J.P.W.: Registration of cervical MRI using multifeature mutual information. IEEE Transactions on Medical Imaging 28(9), 1412–1421 (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Hamrouni, S., Rougon, N., Prêteux, F. (2011). Multi-feature Statistical Nonrigid Registration Using High-Dimensional Generalized Information Measures. In: Fichtinger, G., Martel, A., Peters, T. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2011. MICCAI 2011. Lecture Notes in Computer Science, vol 6891. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23623-5_66
Download citation
DOI: https://doi.org/10.1007/978-3-642-23623-5_66
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23622-8
Online ISBN: 978-3-642-23623-5
eBook Packages: Computer ScienceComputer Science (R0)