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Directional Averages for Motion Segmentation in Discontinuity Preserving Image Registration

  • Christoph JudEmail author
  • Robin Sandkühler
  • Nadia Möri
  • Philippe C. Cattin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10433)

Abstract

The registration of abdominal images is central in the analysis of motion patterns and physiological investigations of abdominal organs. Challenges which arise in this context are discontinuous changes in correspondence across sliding organ boundaries. Standard regularity criteria like smoothness, are not valid in such regions. In this paper, we introduce a novel regularity criterion which incorporates local motion segmentation in order to preserve discontinuous changes in the spatial mapping. Based on local directional statistics of the transformation parameters it is decided which part of a local neighborhood influences a parameter during registration. Thus, the mutual influence of neighboring parameters which are located on opposing sides of sliding organ boundaries is relaxed. The motion segmentation is performed within the regularizer as well as in the image similarity measure and is thus implicitly updated throughout the optimization. In the experiments on the 4DCT POPI dataset we achieve competitive registration performance compared to state-of-the-art methods.

Keywords

Image registration Regularization Motion segmentation 

References

  1. 1.
    Heinrich, M.P., Jenkinson, M., Brady, M., Schnabel, J.A.: MRF-based deformable registration and ventilation estimation of lung CT. IEEE Trans. Med. Imaging 32(7), 1239–1248 (2013)CrossRefGoogle Scholar
  2. 2.
    Hofmann, T., Schölkopf, B., Smola, A.J.: Kernel methods in machine learning. Ann. Stat. 36, 1171–1220 (2008)MathSciNetCrossRefzbMATHGoogle Scholar
  3. 3.
    Jud, C., Möri, N., Bitterli, B., Cattin, P.C.: Bilateral regularization in reproducing kernel hilbert spaces for discontinuity preserving image registration. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, H.-I. (eds.) MLMI 2016. LNCS, vol. 10019, pp. 10–17. Springer, Cham (2016). doi: 10.1007/978-3-319-47157-0_2 CrossRefGoogle Scholar
  4. 4.
    Jud, C., Möri, N., Cattin, P.C.: Sparse kernel machines for discontinuous registration and nonstationary regularization. In: Proceedings of the International Workshop on Biomedical Image Registration, pp. 9–16 (2016)Google Scholar
  5. 5.
    Kiriyanthan, S., Fundana, K., Majeed, T., Cattin, P.C: Discontinuity preserving image registration through motion segmentation: a primal-dual approach. Comput. Math. Methods Med. 2016 (2016). Article ID 9504949Google Scholar
  6. 6.
    Pace, D.F., Aylward, S.R., Niethammer, M.: A locally adaptive regularization based on anisotropic diffusion for deformable image registration of sliding organs. IEEE Trans. Med. Imaging 32(11), 2114–2126 (2013)CrossRefGoogle Scholar
  7. 7.
    Papież, B.W., Heinrich, M.P., Fehrenbach, J., Risser, L., Schnabel, J.A.: An implicit sliding-motion preserving regularisation via bilateral filtering for deformable image registration. Med. Image Anal. 18(8), 1299–1311 (2014)CrossRefGoogle Scholar
  8. 8.
    Polyak, B.T., Juditsky, A.B.: Acceleration of stochastic approximation by averaging. SIAM J. Control Optim. 30(4), 838–855 (1992)MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Preston, J.S., Joshi, S., Whitaker, R.: Deformation estimation with automatic sliding boundary computation. In: Ourselin, S., Joskowicz, L., Sabuncu, M.R., Unal, G., Wells, W. (eds.) MICCAI 2016. LNCS, vol. 9902, pp. 72–80. Springer, Cham (2016). doi: 10.1007/978-3-319-46726-9_9 CrossRefGoogle Scholar
  10. 10.
    Risser, L., Vialard, F.X., Baluwala, H.Y., Schnabel, J.A.: Piecewise-diffeomorphic image registration: application to the motion estimation between 3D CT lung images with sliding conditions. Med. Image Anal. 17(2), 182–193 (2013)CrossRefGoogle Scholar
  11. 11.
    Rueckert, D., Sonoda, L.I., Hayes, C., Hill, D.L., Leach, M.O., Hawkes, D.J.: Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18(8), 712–721 (1999)CrossRefGoogle Scholar
  12. 12.
    Schmidt-Richberg, A., Werner, R., Handels, H., Ehrhardt, J.: Estimation of slipping organ motion by registration with direction-dependent regularization. Med. Image Anal. 16(1), 150–159 (2012)CrossRefGoogle Scholar
  13. 13.
    Shi, W., Jantsch, M., Aljabar, P., Pizarro, L., Bai, W., Wang, H., ORegan, D., Zhuang, X., Rueckert, D.: Temporal sparse free-form deformations. Med. Image Anal. 17(7), 779–789 (2013)CrossRefGoogle Scholar
  14. 14.
    von Siebenthal, M., Székely, G., Gamper, U., Boesiger, P., Lomax, A.J., Cattin, P.C.: 4D MR imaging of respiratory organ motion and its variability. Phys. Med. Biol. 52(6), 1547 (2007)CrossRefGoogle Scholar
  15. 15.
    Vandemeulebroucke, J., Sarrut, D., Clarysse, P.: The POPI-model, a point-validated pixel-based breathing thorax model. In: International Conference on the Use of Computers in Radiation Therapy, vol. 2, pp. 195–199 (2007)Google Scholar
  16. 16.
    Vishnevskiy, V., Gass, T., Szekely, G., Tanner, C., Goksel, O.: Isotropic total variation regularization of displacements in parametric image registration. IEEE Trans. Med. Imaging 36, 385–395 (2016)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Christoph Jud
    • 1
    Email author
  • Robin Sandkühler
    • 1
  • Nadia Möri
    • 1
  • Philippe C. Cattin
    • 1
  1. 1.Department of Biomedical EngineeringUniversity of BaselBaselSwitzerland

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