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Anisotropic filtering of MRI data based upon image gradient histogram

  • Image Processing
  • Conference paper
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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 719))

Abstract

The contribution deals with MR image smoothing via anisotropic diffusion aimed at Signal-to-Noise Ratio increase. For segmentation of the MR data dedicated to 3D image synthesis such a procedure is inevitable. In the contribution a novel function for diffusion coefficient definition, involving the global histogram of relative frequencies of the image intensity gradient, is proposed. The smoothing method developed has been tested on a real MR image (from a set of 2D spin-echo data). A comparison of the influence of both diffusion coefficients on smoothing effect is presented.

The research reported in this contribution was supported, in part, by the Slovak Grant Agency for Science (grant No.2/999003/92) and by the Austrian government within the Ost-West project OWP-51 No.45.208/1-27b/91

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References

  1. G.Gerig et al: Nonlinear anisotropic filtering of MRI Data. IEEE Trans. on Medical Imaging, vol. 11, No. 2, June 1992, 221–232.

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  2. P.Perona and J.Malik: Scale-space and edge detection using anisotropic diffusion. IEEE PAMI, vol.12, No.7, 1990, 629–639.

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  3. I.Bajla et al:A comparison study of smoothing techniques for 3-D image synthesis from MRI data. In: J.Jan(ed): Proc. of the 11-th international conference BIOSIGNAL '92 — IFMBE regional meeting of Danube countries, Brno, Czechoslovakia, June 1992, 31–34.

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Dmitry Chetverikov Walter G. Kropatsch

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© 1993 Springer-Verlag Berlin Heidelberg

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Bajla, I., Marušiak, M., Šrámek, M. (1993). Anisotropic filtering of MRI data based upon image gradient histogram. In: Chetverikov, D., Kropatsch, W.G. (eds) Computer Analysis of Images and Patterns. CAIP 1993. Lecture Notes in Computer Science, vol 719. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-57233-3_11

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  • DOI: https://doi.org/10.1007/3-540-57233-3_11

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-57233-6

  • Online ISBN: 978-3-540-47980-2

  • eBook Packages: Springer Book Archive

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