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Unifying Characterization of Deformable Registration Methods Based on the Inherent Parametrization

An Attempt at an Alternative Analysis Approach

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Biomedical Image Registration (WBIR 2010)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6204))

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Abstract

We propose to characterize deformable registration methods in a unified way, based on their parametrization. In contrast to traditional classifications, we do not apply this characterization only to standard “parametric” methods such as B-Spline Free-form deformations, but we explicitly include elastic and fluid-type “non-parametric” methods, such as the classic variational approach, and the fluid demons method. To this end, we consider parametrizations by linear combinations of arbitrary basis functions. While for the variational approach we simply utilize piecewise linear bases, for the fluid demons method we provide a new interpretation by showing that it can be seen as inherently parametrized by densely located Gaussian basis functions. Furthermore, we show that the semi-implicit discretization of the variational approach can be seen as steepest descent, with a displacement parametrized by densely located bases, based on Green’s functions corresponding to the regularization. This provides a further connection to the demons approaches. The proposed characterization is widely applicable and provides a simple and intuitive way of relating some of the arguably most commonly used methods to each other.

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References

  1. Horn, B., Schunck, B.: Determining optical flow. Artificial Intelligence (1981)

    Google Scholar 

  2. Broit, C.: Optimal Registration of Deformed Images. PhD thesis (1981)

    Google Scholar 

  3. Brown, L.: A survey of image registration techniques. ACM Computing Surveys (1992)

    Google Scholar 

  4. Van den Elsen, P., Pol, E., Viergever, M.: Medical image matching - a review with classification. IEEE Engin. in Medicine and Biology Magazine (1993)

    Google Scholar 

  5. Maintz, J., Viergever, M.: A survey of medical image registration. Medical Image Analysis (1998)

    Google Scholar 

  6. Fitzpatrick, J., Hill, D., Maurer Jr., C.: Image registration. In: Handbook of medical imaging - Medical Image Processing and Analysis (2000)

    Google Scholar 

  7. Hill, D., Batchelor, P., Holden, M., Hawkes, D.: Medical image registration. Physics in Medicine and Biology (2001)

    Google Scholar 

  8. Hajnal, J., Hill, D., Hawkes, D. (eds.): Medical Image Registration. CRC Press, Boca Raton (2001)

    Google Scholar 

  9. Zitova, B., Flusser, J.: Image registration methods: a survey. Image and Vision Computing (2003)

    Google Scholar 

  10. Lester, H., Arridge, S.: A survey of hierarchical non-linear medical image registration. Pattern Recognition (1999)

    Google Scholar 

  11. Cachier, P., Bardinet, E., Dormont, D., Pennec, X., Ayache, N.: Iconic feature based nonrigid registration: the pasha algorithm. Computer Vision and Image Understanding (2003)

    Google Scholar 

  12. Modersitzki, J.: Numerical methods for image registration. Oxford University Press, Oxford (2004)

    MATH  Google Scholar 

  13. Holden, M.: A review of geometric transformations for nonrigid body registration. IEEE Trans. Medical Imaging (2008)

    Google Scholar 

  14. Modersitzki, J.: FAIR: Flexible Algorithms for Image Registration. SIAM, Philadelphia (2009)

    MATH  Google Scholar 

  15. Trouvé, A.: Diffeomorphisms groups and pattern matching in image analysis. Int. Journal of Computer Vision (1998)

    Google Scholar 

  16. Chefd’hotel, C., Hermosillo, G., Faugeras, O.: Flows of diffeomorphisms for multimodal image registration. In: Int. Symp. on Biomedical Imaging (2002)

    Google Scholar 

  17. Chefd’hotel, C.: Geometric Methods in Computer Vision and Image Processing: Contributions and Applications. PhD thesis, L’Ecole Normale Superieure de Cachan (2005)

    Google Scholar 

  18. Beg, M., Miller, M., Trouvé, A., Younes, L.: Computing large deformation metric mappings via geodesic flows of diffeomorphisms. Int. Journal of Computer Vision 61 (2005)

    Google Scholar 

  19. Alvarez, L., Weickert, J., Sánchez, J.: Reliable estimation of dense optical flow fields with large displacements. Int. Journal of Computer Vision (2000)

    Google Scholar 

  20. Khamene, A., Schwarz, L., Zikic, D., Azar, F., Rietzel, E., Navab, N.: A unified and efficient approach for free-form deformable registration. In: Int. Conf. on Computer Vision (2007)

    Google Scholar 

  21. Christensen, G.: Deformable Shape Models for Anatomy. PhD thesis, Washington Uuniversity, Sever Institute of Technology (1994)

    Google Scholar 

  22. Bro-Nielsen, M., Gramkow, C.: Fast fluid registration of medical images. In: Höhne, K.H., Kikinis, R. (eds.) VBC 1996. LNCS, vol. 1131. Springer, Heidelberg (1996)

    Google Scholar 

  23. Pennec, X., Cachier, P., Ayache, N.: Understanding the demon’s algorithm: 3d non-rigid registration by gradient descent. In: Medical Image Computing and Computer Assisted Intervention (1999)

    Google Scholar 

  24. Thirion, J.: Image matching as a diffusion process: an analogy with maxwell’s demons. Medical Image Analysis (1998)

    Google Scholar 

  25. Stefanescu, R., Pennec, X., Ayache, N.: Grid powered nonlinear image registration with locally adaptive regularization. Medical Image Analysis (2004)

    Google Scholar 

  26. Tustison, N., Avants, B., Sundaram, T., Duda, J., Gee, J.: A generalization of free-form deformation image registration within the itk finite element framework. In: Workshop on Biomedical Image Registration (2006)

    Google Scholar 

  27. Feldmar, J., Declerck, J., Malandain, G., Ayache, N.: Extension of the icp algorithm to non-rigid intensity-based registration of 3d volumes. Computer Vision and Image Understanding (1997)

    Google Scholar 

  28. Declerck, J., Feldmar, J., Goris, M., Fabienne, B.: Automatic registration and alignment on a template of cardiac stress rest reoriented spect images. IEEE Trans. Medical Imaging (1997)

    Google Scholar 

  29. Rueckert, D., Sonoda, L., Hayes, C., Hill, D., Leach, M., Hawkes, D.: Nonrigid registration using free-form deformations: application to breast mr images. IEEE Trans. Medical Imaging (1999)

    Google Scholar 

  30. Kybic, J., Unser, M.: Fast parametric elastic image registration. IEEE Trans. Image Processing (2003)

    Google Scholar 

  31. Rohlfing, T., Maurer, C.R., Bluemke, D.J., Jacobs, M.: Volume-preserving nonrigid registration of mr breast images using free-form deformation with an incompressibility constraint. IEEE Trans. Medical Imaging (2003)

    Google Scholar 

  32. Glocker, B., Komodakis, N., Tziritas, G., Navab, N., Paragios, N.: Dense image registration through mrfs and efficient linear programming. Medical Image Analysis (2008)

    Google Scholar 

  33. Klein, S., Staring, M., Murphy, K., Viergever, M., Pluim, J.: Elastix: A toolbox for intensity-based medical image registration. IEEE Trans. Medical Imaging (2010)

    Google Scholar 

  34. Unser, M., Aldroubi, A., Eden, M.: B-spline signal processing. part i. theory. IEEE Trans. Signal Processing (1993)

    Google Scholar 

  35. Unser, M., Aldroubi, A., Eden, M.: B-spline signal processing. part ii. efficient design and applications. IEEE Trans. Signal Processing (1993)

    Google Scholar 

  36. Amit, Y.: A nonlinear variational problem for image matching. SIAM Journal on Scientific Computing (1994)

    Google Scholar 

  37. Christensen, G., Johnson, H.: Consistent image registration. IEEE Trans. Medical Imaging (2001)

    Google Scholar 

  38. Ashburner, J., Friston, K.: Nonlinear spatial normalization using basis functions. Human Brain Mapping (1999)

    Google Scholar 

  39. Hermosillo, G., Chefd’Hotel, C., Faugeras, O.: Variational methods for multimodal image matching. Int. Journal of Computer Vision (2002)

    Google Scholar 

  40. Thirion, J.: Non-rigid matching using demons. Computer Vision and Pattern Recognition (1996)

    Google Scholar 

  41. Vercauteren, T., Pennec, X., Perchant, A., Nicholas, A.: Diffeomorphic demons: Efficient non-parametric image registration. NeuroImage (2009)

    Google Scholar 

  42. Cahill, N., Noble, J., Hawkes, D.: Demons algorithms for fluid and curvature registration. Int. Symp. on Biomedical Imaging (2009)

    Google Scholar 

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Zikic, D., Kamen, A., Navab, N. (2010). Unifying Characterization of Deformable Registration Methods Based on the Inherent Parametrization. In: Fischer, B., Dawant, B.M., Lorenz, C. (eds) Biomedical Image Registration. WBIR 2010. Lecture Notes in Computer Science, vol 6204. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14366-3_15

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  • DOI: https://doi.org/10.1007/978-3-642-14366-3_15

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-14365-6

  • Online ISBN: 978-3-642-14366-3

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