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Point-based elastic registration of medical image data using approximating thin-plate splines

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Visualization in Biomedical Computing (VBC 1996)

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Abstract

We consider elastic registration of medical image data based on thin-plate splines using a set of corresponding anatomical point landmarks. Previous work on this topic has concentrated on using interpolation schemes. Such schemes force the corresponding landmarks to exactly match each other and assume that the landmark positions are known exactly. However, in real applications the localization of landmarks is always prone to some error. Therefore, to take into account these localization errors, we have investigated the application of an approximation scheme which is based on regularization theory. This approach generally leads to a more accurate and robust registration result. In particular, outliers do not disturb the registration result as much as is the case with an interpolation scheme. Also, it is possible to individually weight the landmarks according to their localization uncertainty. In addition to this study, we report on investigations into semi-automatic extraction of anatomical point landmarks.

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References

  1. N. Arad, N. Dyn, D. Reisfeld, and Y. Yeshurun. Image warping by radial basis functions: Application to facial expressions. Computer Vision, Graphics, and Image Processing, 56(2):161–172, 1994.

    Google Scholar 

  2. F. Bookstein. Principal warps: Thin-plate splines and the decomposition of deformations. IEEE Trans. Pattern Anal. and Machine Intell., 11(6):567–585, 1989.

    MATH  Google Scholar 

  3. F. Bookstein. Thin-plate splines and the atlas problem for biomedical images. In A. Colchester and D. Hawkes, editors, 12th Internat. Conf. Information Processing in Medical Imaging, Volume 511 of Lecture Notes in Computer Science, pages 326–342, Wye/UK, 1991. Springer-Verlag Berlin Heidelberg.

    Google Scholar 

  4. H. Chang and J. Fitzpatrick. A technique for accurate magnetic resonance imaging in the presence of field inhomogeneities. IEEE Trans. Med. Imaging, 11:319, 1992.

    Article  Google Scholar 

  5. J. Duchon. Interpolation des fonctions de deux variables suivant le principle de la flexion des plaques minces. R.A.I.R.O. Analyse Numérique, 10(12), 1976.

    Google Scholar 

  6. A. Evans, W. Dai, L. Collins, P. Neelin, and S. Marrett. Warping of a computerized 3-d atlas to match brain image volumes for quantitative neuroanatomical and functional analysis. In M. Loew, editor, Medical Imaging V: Image Processing, volume 1445 of Proc. SPIE, pages 236–246, San Jose, CA, 1991.

    Google Scholar 

  7. W. Förstner. A feature based correspondence algorithm for image matching. Intern. Arch. of Photogrammetry and Remote Sensing, 26-3/3:150–166, 1986.

    Google Scholar 

  8. D. Hill, D. Hawkes, J. Crossman, M. Gleeson, T. Cox, E. Bracey, A. Strong, and P. Graves. Registration of MR and CT images for skull base surgery using pointlike anatomical features. The British J. of Radiology, 64(767):1030–1035, 1991.

    Google Scholar 

  9. K. Lüdeke, P. Röschmann, and R. Tischler. Susceptibility artifacts in nmr imaging. MRI, 3:329, 1985.

    Google Scholar 

  10. K. Mardia and J. Little. Image warping using derivative information. In F. Bookstein, J. Duncan, N. Lange, and D. Wilson, editors, Mathematical Methods in Medical Imaging III, volume 2299 of Proc. SPIE, pages 16–31, San Diego, CA, 25–26 July 1994.

    Google Scholar 

  11. J. Michiels, H. Bosmans, P. Pelgrims, D. Vandermeulen, J. Gybels, G. Marchal, and P. Suetens. On the problem of geometric distortion in magnetic resonance images for stereotactic neurosurgery. Mag. Res. Imag., 12:749, 1994.

    Google Scholar 

  12. D. W. Paglieroni. A unified transform algorithm and architecture. Machine Vision and Applications, 5(1):47–55, 1992.

    Article  Google Scholar 

  13. K. Rohr. Localization properties of direct corner detectors. J. of Mathematical Imaging and Vision, 4(2):139–150, 1994.

    MathSciNet  Google Scholar 

  14. K. Rohr, H. S. Stiehl, R. Sprengel, W. Beil, T. M. Buzug, J. Weese, and M. H. Kuhn. Point-based elastic registration of medical image data using approximating thin-plate splines. Techn. Report FBI-HH-M-254/96, FB Informatik, Universität Hamburg, Febr. 1996.

    Google Scholar 

  15. R. Sprengel, K. Rohr, and H. S. Stiehl. Thin-plate spline approximation for image registration. 1996. submitted for publication.

    Google Scholar 

  16. J.-P. Thirion. Extremal points: definition and application to 3d image registration. In Proc. IEEE Conf. on Computer Vision and Pattern Recognition, pages 587–592, Seattle/Washington, USA, 1994.

    Google Scholar 

  17. G. Wahba. Spline Models for Observational Data. Society for Industrial and Applied Mathematics, Philadelphia, Pennsylvania, 1990.

    Google Scholar 

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Karl Heinz Höhne Ron Kikinis

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© 1996 Springer-Verlag Berlin Heidelberg

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Rohr, K. et al. (1996). Point-based elastic registration of medical image data using approximating thin-plate splines. In: Höhne, K.H., Kikinis, R. (eds) Visualization in Biomedical Computing. VBC 1996. Lecture Notes in Computer Science, vol 1131. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0046967

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  • DOI: https://doi.org/10.1007/BFb0046967

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61649-8

  • Online ISBN: 978-3-540-70739-4

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