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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Laib, A., Beuf, O., Issever, A., Newitt, D.C., Majumdar, S.: Direct measures of trabecu-lar bone architecture from MR images. Adv. Exp. Med. Biol. 496, 37–46 (2001)
Maragos, P.: Pattern spectrum and multiscale shape representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11(7), 701–716 (1989)
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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
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