Abstract
Non-rigid image registration using free-form deformations (FFD) is a widely used technique in medical image registration. The balance between robustness and accuracy is controlled by the control point grid spacing and the amount of regularization. In this paper, we revisit the classic FFD registration approach and propose a sparse representation for FFDs using the principles of compressed sensing. The sparse free-form deformation model (SFFD) can capture fine local details such as motion discontinuities without sacrificing robustness. We demonstrate the capabilities of the proposed framework to accurately estimate smooth as well as discontinuous deformations in 2D and 3D image sequences. Compared to the classic FFD approach, a significant increase in registration accuracy can be observed in natural images (61%) as well as in cardiac MR images (53%) with discontinuous motions.
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Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L.G., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Transactions on Medical Imaging 18(8), 712–721 (1999)
Modat, M., Ridgway, G.R., Taylor, Z.A., Lehmann, M., Barnes, J., Hawkes, D.J., Fox, N.C., Ourselin, S.: Fast free-form deformation using graphics processing units. Computer Methods and Programs in Biomedicine 98(3), 278–284 (2010)
Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.: Elastix: a toolbox for intensity-based medical image registration. IEEE Transactions on Medical Imaging 29(1), 196–205 (2010)
Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17(1-3), 185–203 (1981)
Mémin, E., Pérez, P.: Hierarchical estimation and segmentation of dense motion fields. International Journal of Computer Vision 46(2), 129–155 (2002)
Roth, S., Black, M.J.: On the spatial statistics of optical flow. In: ICCV 2005, vol. 1, pp. 42–49 (2005)
Sun, D., Roth, S., Black, M.J.: Secrets of optical flow estimation and their principles. In: 2010 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2432–2439. IEEE (2010)
Thirion, J.P.: Image matching as a diffusion process: an analogy with maxwell’s demons. Medical Image Analysis 2(3), 243–260 (1998)
Vercauteren, T., Pennec, X., Perchant, A., Ayache, N.: Symmetric Log-Domain Diffeomorphic Registration: A Demons-Based Approach. In: Metaxas, D., Axel, L., Fichtinger, G., Székely, G. (eds.) MICCAI 2008, Part I. LNCS, vol. 5241, pp. 754–761. Springer, Heidelberg (2008)
Schnabel, J.A., Rueckert, D., Quist, M., Blackall, J.M., Castellano-Smith, A.D., Hartkens, T., Penney, G.P., Hall, W.A., Liu, H., Truwit, C.L., Gerritsen, F.A., Hill, D.L.G., Hawkes, D.J.: A Generic Framework for Non-rigid Registration Based on Non-uniform Multi-level Free-Form Deformations. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 573–581. Springer, Heidelberg (2001)
Rohlfing, T., Maurer Jr., C.R.: Intensity-Based Non-rigid Registration Using Adaptive Multilevel Free-Form Deformation with an Incompressibility Constraint. In: Niessen, W.J., Viergever, M.A. (eds.) MICCAI 2001. LNCS, vol. 2208, pp. 111–119. Springer, Heidelberg (2001)
Hansen, M.S., Larsen, R., Glocker, B., Navab, N.: Adaptive parametrization of multivariate b-splines for image registration. In: CVPR, pp. 1–8 (2008)
Roozgard, A., Barzigar, N., Cheng, S., Verma, P.: Dense image registration using sparse coding and belief propagation. In: International Conference on Signal Processing and Communication Systems, pp. 1–5 (2011)
Shen, X., Wu, Y.: Sparsity model for robust optical flow estimation at motion discontinuities. In: CVPR, pp. 2456–2463 (2010)
Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through mrfs and efficient linear programming. Medical Image Analysis 12(6), 731–741 (2008)
Baker, S., Scharstein, D., Lewis, J.P., Roth, S., Black, M.J., Szeliski, R.: A database and evaluation methodology for optical flow. In: ICCV, pp. 1–8 (2007)
Donoho, D.L., Huo, X.: Uncertainty principles and ideal atomic decomposition. IEEE Transactions on Information Theory 47(7), 2845–2862 (2001)
Studholme, C., Hill, D., Hawkes, D., et al.: An overlap invariant entropy measure of 3D medical image alignment. Pattern Recognition 32(1), 71–86 (1999)
Kim, S.J., Koh, K., Lustig, M., Boyd, S., Gorinevsky, D.: An interior-point method for large-scale l1-regularized least squares. IEEE Journal of Selected Topics in Signal Processing 1(4), 606–617 (2007)
Kybic, J., Unser, M.: Fast parametric elastic image registration. IEEE Transactions on Image Processing 12(11), 1427–1442 (2003)
Pizarro, L., Delpiano, J., Aljabar, P., Ruiz-del-Solar, J., Rueckert, D.: Towards dense motion estimation in light and electron microscopy. In: ISBI: From Nano to Macro, pp. 1939–1942 (2011)
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Shi, W. et al. (2012). Registration Using Sparse Free-Form Deformations. In: Ayache, N., Delingette, H., Golland, P., Mori, K. (eds) Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012. MICCAI 2012. Lecture Notes in Computer Science, vol 7511. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33418-4_81
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DOI: https://doi.org/10.1007/978-3-642-33418-4_81
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