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Deformable image registration with geometric changes

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Geometric changes present a number of difficulties in deformable image registration. In this paper, we propose a global deformation framework to model geometric changes whilst promoting a smooth transformation between source and target images. To achieve this, we have developed an innovative model which significantly reduces the side effects of geometric changes in image registration, and thus improves the registration accuracy. Our key contribution is the introduction of a sparsity-inducing norm, which is typically L1 norm regularization targeting regions where geometric changes occur. This preserves the smoothness of global transformation by eliminating local transformation under different conditions. Numerical solutions are discussed and analyzed to guarantee the stability and fast convergence of our algorithm. To demonstrate the effectiveness and utility of this method, we evaluate it on both synthetic data and real data from traumatic brain injury (TBI). We show that the transformation estimated from our model is able to reconstruct the target image with lower instances of error than a standard elastic registration model.

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  • Bajcsy, R., Broit, C., 1982. Matching of deformed images. Proc. 6th Int. Conf. on Pattern Recognition, p.351–353.

    Google Scholar 

  • Beck, A., Teboulle, M., 2008. A fast iterative shrinkagethresholding algorithm for linear inverse problems. SIAM J. Imag. Sci., 2(1):183–202. [doi:10.1137/080716542]

    Article  MathSciNet  Google Scholar 

  • Beg, M.F., Miller, M.I., Trouvé, A., et al., 2005. Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. J. Comput. Vis., 61(2):139–157. [doi:10.1023/B:VISI.0000043755.93987.aa]

    Article  Google Scholar 

  • Chambolle, A., 2004. An algorithm for total variation minimization and applications. J. Math. Imag. Vis., 20(1): 89–97. [doi:10.1023/B:JMIV.0000011325.36760.1e]

    MathSciNet  Google Scholar 

  • Christensen, G.E., Johnson, H.J., 2001. Consistent image registration. IEEE Trans. Med. Imag., 20(7):568–582. [doi:10.1109/42.932742]

    Article  Google Scholar 

  • Christensen, G.E., Rabbitt, R.D., Miller, M.I., 1996. Deformable templates using large deformation kinematics. IEEE Trans. Image Process., 5(10):1435–1447. [doi:10.1109/83.536892]

    Article  Google Scholar 

  • Hall, E.L., 1979. Computer Image Processing and Recognition. Academic Press, New York, USA.

    MATH  Google Scholar 

  • Herbin, M., Venot, A., Devaux, J.Y., et al., 1989. Automated registration of dissimilar images: application to medical imagery. Comput. Vis. Graph. Image Process., 47(1): 77–88. [doi:10.1016/0734–189X(89)90055–8]

    Article  Google Scholar 

  • Hernandez, M., Olmos, S., Pennec, X., 2008. Comparing algorithms for diffeomorphic registration: stationary LDDMM and diffeomorphic demons. Proc. 2nd MICCAI Workshop on Mathematical Foundations of Computational Anatomy, p.24–35.

    Google Scholar 

  • Lucas, B.D., Kanade, T., 1981. An iterative image registration technique with an application to stereo vision. Proc. 7th Int. Joint Conf. on Artificial Intelligence, p.121–130.

    Google Scholar 

  • Luck, J., Little, C., Hoff, W., 2000. Registration of range data using a hybrid simulated annealing and iterative closest point algorithm. Proc. IEEE Int. Conf. on Robotics and Automation, p.3739–3744. [doi:10.1109/ROBOT.2000.845314]

    Google Scholar 

  • Niethammer, M., Hart, G.L., Pace, D.F., et al., 2011. Geometric metamorphosis. Proc. 14th Int. Conf. on Medical Image Computing and Computer-Assisted Intervention, p.639–646. [doi:10.1007/978–3-642–23629–7_78]

    Google Scholar 

  • Richard, F.J.P., Samson, A.M.M., 2007. Metropolis-Hasting techniques for finite-element-based registration. Proc. IEEE Conf. on Computer Vision and Pattern Recognition, p.1–6. [doi:10.1109/CVPR.2007.383422]

    Google Scholar 

  • Rudin, L.I., Osher, S., Fatemi, E., 1992. Nonlinear total variation based noise removal algorithms. Phys. D, 60(1–4): 259–268. [doi:10.1016/0167–2789(92)90242-F]

    Article  MATH  Google Scholar 

  • Trouvé, A., Younes, L., 2005. Metamorphoses through Lie group action. Found. Comput. Math., 5(2):173–198. [doi:10.1007/s10208–004-0128-z]

    Article  MathSciNet  MATH  Google Scholar 

  • Zhang, M., Singh, N., Fletcher, P.T., 2013. Bayesian estimation of regularization and atlas building in diffeomorphic image registration. Proc. 23rd Int. Conf. on Information Processing in Medical Imaging. p.37–48. [doi:10.1007/978–3-642–38868–2_4]

    Chapter  Google Scholar 

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Correspondence to Bo Zhu.

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Liu, Y., Zhu, B. Deformable image registration with geometric changes. Frontiers Inf Technol Electronic Eng 16, 829–837 (2015).

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