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Surrogate Measures of Thickness in the Regime of Limited Image Resolution: Part 2: Granulometry

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Artificial Intelligence and Soft Computing (ICAISC 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7268))

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Abstract

In the present study a granulometry-based method of computing surrogate measure of thickness from gray-level images is introduced. Using Bland-Altman analysis it is demonstrated for a set of 25 μCT images that the difference between surrogate and reference measures of thickness corresponds to some non-zero bias. Analytical formulas derived in this study identify conditions necessary for the equality of surrogate measures of thickness and real thickness. The performance of the proposed method in the presence of image degradation factors (resolution decrease and noise) is tested.

The paper is supported by project funded from 2009–2012 resources for science as a research project.

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References

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Author information

Authors and Affiliations

  1. Cracow University of Technology, Al. Jana Pawa II 37, 31-864, Cracow, Poland

    Zbisław Tabor & Rafał Petryniak

Authors
  1. Zbisław Tabor
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  2. Rafał Petryniak
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Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Mickiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, Computer Science Division, University of California Berkeley, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Computational Intelligence Laboratory, Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

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© 2012 Springer-Verlag Berlin Heidelberg

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Tabor, Z., Petryniak, R. (2012). Surrogate Measures of Thickness in the Regime of Limited Image Resolution: Part 2: Granulometry. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2012. Lecture Notes in Computer Science(), vol 7268. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29350-4_43

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  • DOI: https://doi.org/10.1007/978-3-642-29350-4_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29349-8

  • Online ISBN: 978-3-642-29350-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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