Abstract
A potentially large anatomical variability among subjects in a population makes nonrigid image registration techniques prone to inaccuracies and to high computational costs in their optimisation. In this paper, we propose a new learning-based approach to accelerate the convergence rate of any chosen parametric energy-based image registration method. From a set of training images and their corresponding deformations, our method learns offline a projection from the gradient space of the energy functional to the parameter space of the chosen registration method using partial least squares. Combined with a regularisation term, the learnt projection is subsequently used online to approximate the optimisation of the energy functional for unseen images. We employ the B-spline approach as underlying registration method, but other parametric methods can be used as well. We perform experiments on synthetic image data and MR cardiac sequences to show that our approach significantly accelerates the convergence -in number of iterations and total computational cost- of the chosen registration method, while achieving similar results in terms of accuracy.
This work was partially funded by CONICYT.
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References
Horn, B.K.P., Schunck, B.G.: Determining optical flow. Artificial Intelligence 17(1-3), 185–203 (1981)
Christensen, G.E., Rabbitt, R.D., Miller, M.I.: Deformable templates using large deformation kinematics. IEEE Transactions on Image Processing 5(10), 1435–1447 (1996)
Thirion, J.P.: Image matching as a diffusion process: an analogy with Maxwell’s demons. Medical Image Analysis 2(3), 243–260 (1998)
Beg, M.F., Miller, M.I., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. International Journal of Computer Vision 61(2), 139–157 (2005)
Ashburner, J.: A fast diffeomorphic image registration algorithm. NeuroImage 38(1), 95–113 (2007)
Little, J.A., Hill, D.L.G., Hawkes, D.J.: Deformations incorporating rigid structures. In: Computer Vision and Image Understanding, vol. 66, pp. 223–232 (1997)
Yoshida, H.: Removal of normal anatomic structures in radiographs using wavelet-based nonlinear variational method for image matching. In: Proceedings of SPIE, vol. 3458, p. 174 (1998)
Ashburner, J., Friston, K.J.: Nonlinear spatial normalization using basis functions. Human Brain Mapping 7(4), 254–266 (1999)
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)
Wu, Y.T., Kanade, T., Li, C.C., Cohn, J.: Image registration using wavelet-based motion model. International Journal of Computer Vision 38(2), 129–152 (2000)
Fornefett, M., Rohr, K., Stiehl, H.S.: Radial basis functions with compact support for elastic registration of medical images. Image and Vision Computing 19(1), 87–96 (2001)
Xue, Z., Shen, D., Davatzikos, C.: Statistical representation of high-dimensional deformation fields with application to statistically constrained 3D warping. Medical Image Analysis 10(5), 740–751 (2006)
Loeckx, D., Maes, F., Vandermeulen, D., Suetens, P.: Temporal subtraction of thorax CR images using a statistical deformation model. IEEE Transactions on Medical Imaging 22(11), 1490–1504 (2003)
Rueckert, D., Frangi, A.F., Schnabel, J.A.: Automatic construction of 3-D statistical deformation models of the brain using nonrigid registration. IEEE Transactions on Medical Imaging 22(8), 1014–1025 (2003)
Tang, S., Fan, Y., Wu, G., Kim, M., Shen, D.: RABBIT: rapid alignment of brains by building intermediate templates. NeuroImage 47(4), 1277–1287 (2009)
Pszczolkowski, S., Pizarro, L., Guerrero, R., Rueckert, D.: Nonrigid free-form registration using landmark-based statistical deformation models. In: Proceedings of SPIE, vol. 8314, p. 831418 (2012)
Tian, Y., Narasimhan, S.G.: A globally optimal data-driven approach for image distortion estimation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1277–1284. IEEE (2010)
Batmanghelich, N., Taskar, B., Davatzikos, C.: Generative-Discriminative basis learning for medical imaging. IEEE Transactions on Medical Imaging (2011)
Wu, G., Qi, F., Shen, D.: Learning Best Features and Deformation Statistics for Hierarchical Registration of MR Brain Images. In: Karssemeijer, N., Lelieveldt, B. (eds.) IPMI 2007. LNCS, vol. 4584, pp. 160–171. Springer, Heidelberg (2007)
Ablitt, N.A., Gao, J., Keegan, J., Stegger, L., Firmin, D.N., Yang, G.Z.: Predictive cardiac motion modeling and correction with partial least squares regression. IEEE Transactions on Medical Imaging 23(10), 1315–1324 (2004)
Kim, M., Wu, G., Yap, P., Shen, D.: A General Fast Registration framework by learning deformation-appearance correlation. IEEE Transactions on Image Processing (99), 1823–1833 (2011)
Zikic, D., Kamen, A., Navab, N.: Natural gradients for deformable registration. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2847–2854. IEEE (2010)
Klein, S., Staring, M., Andersson, P., Pluim, J.P.W.: Preconditioned Stochastic Gradient Descent Optimisation for Monomodal Image Registration. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol. 6892, pp. 549–556. Springer, Heidelberg (2011)
Wahba, G.: Spline models for observational data, vol. 59. Society for Industrial Mathematics (1990)
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Pszczolkowski, S., Pizarro, L., O’Regan, D.P., Rueckert, D. (2012). Gradient Projection Learning for Parametric Nonrigid Registration. In: Wang, F., Shen, D., Yan, P., Suzuki, K. (eds) Machine Learning in Medical Imaging. MLMI 2012. Lecture Notes in Computer Science, vol 7588. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35428-1_28
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DOI: https://doi.org/10.1007/978-3-642-35428-1_28
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