Abstract
Deformable image registration is an important step in medical image analysis. It enables an automatic labelling of anatomical structures using atlas-based segmentation, motion compensation and multi-modal fusion. The use of discrete optimisation approaches has recently attracted a lot attention for mainly two reasons. First, they are able to find an approximate global optimum of the registration cost function and can avoid false local optima. Second, they do not require a derivative of the similarity metric, which increases their flexibility. However, the necessary quantisation of the deformation space causes a very large number of degrees of freedom with a high computational complexity. To deal with this, previous work has focussed on parametric transformation models. In this work, we present an efficient non-parametric discrete registration method using a filter-based similarity cost aggregation and a decomposition of similarity and regularisation term into two convex optimisation steps. This approach enables non-parametric registration with billions of degrees of freedom with computation times of less than a minute. We apply our method to two different common medical image registration tasks, intra-patient 4D-CT lung motion estimation and inter-subject MRI brain registration for segmentation propagation. We show improvements on current state-of-the-art performance both in terms of accuracy and computation time.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Avants, B.B., Tustison, N.J., Song, G., Cook, P.A., Klein, A., Gee, J.C.: A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54(3), 2033–2044 (2011)
Cachier, P., Bardinet, E., Dormont, D., Pennec, X., Ayache, N.: Iconic feature based nonrigid registration: The PASHA algorithm. Comput. Vis. Image Underst. 89(2-3), 272–298 (2003)
Castillo, R., Castillo, E., Guerra, R., Johnson, V.E., McPhail, T., Garg, A.K., Guerrero, T.: A framework for evaluation of deformable image registration spatial accuracy using large landmark point sets. Phys. Med. Biol. 54(7), 1849 (2009)
Caviness Jr., V.S., Meyer, J., Makris, N., Kennedy, D.N.: MRI-based Topographic Parcellation of Human Neocortex: An Anatomically Specified Method with Estimate of Reliability. Journal of Cognitive Neuroscience 8(6), 566–587 (1996)
Chambolle, A.: An algorithm for total variation minimization and applications. Journal of Mathematical Imaging and Vision 20(1-2), 89–97 (2004)
Christensen, G.E., Johnson, H.J.: Consistent Image Registration. IEEE Trans. Med. Imag. 20(7), 568–582 (2001)
Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through MRFs and efficient linear programming. Med. Imag. Anal. 12(6), 731–741 (2008)
Heinrich, M.P., Jenkinson, M., Papież, B.W., Brady, M., Schnabel, J.A.: Towards Realtime Multimodal Fusion for Image-Guided Interventions Using Self-Similarities. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol. 8149, pp. 187–194. Springer, Heidelberg (2013)
Heinrich, M.P., Jenkinson, M., Brady, M., Schnabel, J.A.: MRF-based Deformable Registration and Ventilation Estimation of Lung CT. IEEE Trans. Med. Imag. 32(7), 1239–1248 (2013)
Heinrich, M.P.: Deformable lung registration for pulmonary image analysis of MRI and CT scans. University of Oxford (2013)
Hermann, S., Werner, R.: High Accuracy Optical Flow for 3D Medical Image Registration Using the Census Cost Function. In: Klette, R., Rivera, M., Satoh, S. (eds.) PSIVT 2013. LNCS, vol. 8333, pp. 23–35. Springer, Heidelberg (2014)
Hosni, A., Rhemann, C., Bleyer, M., Rother, C., Gelautz, M.: Fast Cost-Volume Filtering for Visual Correspondence and Beyond. IEEE Trans. Pattern Anal. Mach. Intell. 35(2), 504–511 (2013)
Klein, A., et al.: Evaluation of 14 nonlinear deformation algorithms applied to human brain MRI registration. Neuroimage 46(3), 786–802 (2008)
Lewis, J.P.: Fast normalized cross-correlation. Vision Interface 10(1), 120–123 (1995)
Lorenzi, M., Ayache, N., Frisoni, G.B., Pennec, X.: LCC-Demons: a robust and accurate diffeomorphic registration algorithm. NeuroImage 81, 470–483 (2013)
Papież, B.W., Heinrich, M.P., Risser, L., Schnabel, J.A.: Complex Lung Motion Estimation via Adaptive Bilateral Filtering of the Deformation Field. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part III. LNCS, vol. 8151, pp. 25–32. Springer, Heidelberg (2013)
Popuri, K., Cobzas, D., Jägersand, M.: A Variational Formulation for Discrete Registration. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part III. LNCS, vol. 8151, pp. 187–194. Springer, Heidelberg (2013)
Rühaak, J., Heldmann, S., Kipshagen, T., Fischer, B.: Highly Accurate Fast Lung CT Registration. In: Ourselin, S., Haynor, D.R. (eds.) SPIE Medical Imaging, pp. 1–9 (2013)
Scharstein, D., Szeliski, R.: A taxonomy and evaluation of dense two-frame stereo correspondence algorithms. Int. J. Comput. Vision 47(1), 7–42 (2002)
So, R.W.K., Tang, T.W.H., Chung, A.C.S.: Non-rigid image registration of brain magnetic resonance images using graph-cuts. Pattern Recognition 44(10-11), 2450–2467 (2011)
Sotiras, A., Davatzikos, C., Paragios, N.: Deformable Medical Image Registration: A Survey. IEEE Trans. Med. Imag. 32(7), 1153–1190 (2013)
Steinbrücker, F., Pock, T., Cremers, D.: Large displacement optical flow computation without warping. In: ICCV 2009, pp. 1609–1614 (2009)
Veksler, O.: Fast Variable Window for Stereo Correspondence using Integral Images. In: CVPR 2003, pp. 1–6 (2003)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Diffeomorphic demons: Efficient non-parametric image registration. NeuroImage 45(1), 61–72 (2009)
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
Heinrich, M.P., Papież, B.W., Schnabel, J.A., Handels, H. (2014). Non-parametric Discrete Registration with Convex Optimisation. In: Ourselin, S., Modat, M. (eds) Biomedical Image Registration. WBIR 2014. Lecture Notes in Computer Science, vol 8545. Springer, Cham. https://doi.org/10.1007/978-3-319-08554-8_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-08554-8_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-08553-1
Online ISBN: 978-3-319-08554-8
eBook Packages: Computer ScienceComputer Science (R0)