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Self-similarity of Images in the Fourier Domain, with Applications to MRI

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Image Analysis and Recognition (ICIAR 2008)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 5112))

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Abstract

Results presented in this paper represent part of an ongoing research programme dedicated to the resolution enhancement of Fourier domain magnetic resonance (MR) data. Here we explore the use of self-similarity methods that may aid in frequency extrapolation of such data. To this end, we present analytical and empirical results demonstrating the self similarity of complex, Fourier domain MR data.

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Author information

Authors and Affiliations

  1. Department of Applied Mathematics, Faculty of Mathematics, University of Waterloo, Waterloo, Ontario, Canada, N2L 3G1

    G. S. Mayer & Edward R. Vrscay

  2. The Seaman Family MR Research Centre, Calgary, Alberta, Canada, T2N 2T9

    M. L. Lauzon, B. G. Goodyear & J. R. Mitchell

  3. The Department of Radiology, University of Calgary, Alberta, Canada, T2N 1N4

    B. G. Goodyear

Authors
  1. G. S. Mayer
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  2. Edward R. Vrscay
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  3. M. L. Lauzon
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  4. B. G. Goodyear
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  5. J. R. Mitchell
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Editor information

Aurélio Campilho Mohamed Kamel

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© 2008 Springer-Verlag Berlin Heidelberg

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Mayer, G.S., Vrscay, E.R., Lauzon, M.L., Goodyear, B.G., Mitchell, J.R. (2008). Self-similarity of Images in the Fourier Domain, with Applications to MRI. In: Campilho, A., Kamel, M. (eds) Image Analysis and Recognition. ICIAR 2008. Lecture Notes in Computer Science, vol 5112. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69812-8_5

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  • DOI: https://doi.org/10.1007/978-3-540-69812-8_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-69811-1

  • Online ISBN: 978-3-540-69812-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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