Fast Granulometry Operator for the 3D Identification of Cell Structures

• Autorzy: Brun E. , Ferrero C. , Vicente J.

Identyfikatory

DOI

10.3233/FI-2017-1590

• Języki publikacji: EN

Abstrakty

EN

The granulometry operator is a mathematical operator largely employed in the 3D analysis of porous media to estimate the sizes of the pores detected in pervious materials and tissues. Quantifying the total porosity volume in a material with only closed pores is a relatively easy task. A simple numerical analysis of connected void or fluid phase components enables one to obtain such a volume. Unfortunately, for materials and/or tissues with (partly) open porosity granulometry calculations might become excessively time and memory consuming. In this work we suggest a method by means of which the open porosity map can be rapidly calculated on the basis of a pre-calculated distance map.

Słowa kluczowe

EN

mathematical morphology; granulometry; 3D images

• Wydawca: IOS Press
• Czasopismo: Fundamenta Informaticae
• Rocznik: 2017
• Tom: Vol. 155, nr 4
• Strony: 363--372
• Opis fizyczny: Bibliogr. 11 poz., rys., tab., wykr.

Twórcy

autor

Brun E.

• Universite Grenoble Alpes, Rayonnement Synchrotron et Recherche Medicale EA7442, 71, Avenue des Martyrs, CS40220, F-38043, Grenoble Cedex 9, France

autor

Ferrero C.

• ESRF - The European Synchrotron, 71, Avenue des Martyrs, CS40220, F-38043, Grenoble, France

autor

Vicente J.

• IUSTI, UMR CNRS 7607, Universite AMU, Marseille, France

Bibliografia

• [1] Matheron. Elements pour une théorie des milieux poreux. Masson, 1967. URL https://books.google.pl/books?id=wNslAAAAMAAJ.
• [2] Soille P. Morphological Image Analysis: Principles and Applications. Springer-Verlag New York, Inc., 2003. ISBN 3540429883.
• [3] Serra J. Image Analysis and mathematical Morphology. Academic press, 1982. ISBN:0126372403, 9780126372403.
• [4] Vincent L. Fast grayscale granulometry algorithms. In: International Symposium on Mathematical Morphology. Computational Imaging and Vision, vol. 2, 1994, pp 265-272. doi:10.1007/978-94-011-1040-2_34.
• [5] Vincent L. Granulometries and Opening trees. Fundamenta Informaticae, 2000;41(1-2):57–90.
• [6] Sethian JA. Level Set Methods and Fast Marching Methods. In: Cambridge Monographs on applied and computational Mathematics. Cambridge University Press, 1999. ISBN:9780521645577.
• [7] Detrixhe M, Gibou F, Min C. A parallel fast sweeping method for the Eikonal equation. Journal of Computational Physics, 2013;237:46–55. URL https://doi.org/10.1016/j.jcp.2012.11.042.
• [8] Meyer F, Beucher S. Morphological segmentation. Journal of Visual Communication and Image Representation, 1990;1(1):21–46. URL https://doi.org/10.1016/1047-3203(90)90014-M.
• [9] Beucher S. Algorithmes sans biais de la ligne de partage des eaux, 2004.
• [10] Beucher S. Numerical residues. Image Vision Comput., 2007;25(4):405–415. URL https://doi.org/10.1016/j.imavis.2006.07.020.
• [11] Brun E, Vicente J, Topin F, Occelli R. IMorph : A 3D morphological tool to fully analyse all kind of cellular materials. In: Cellmet 08. 2008.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).