Numerical Algorithm for Pore Size Distribution Characterization of Materials from 3D Images

Neven Ukrainczyk; Eduard Koenders; Klaas van Breugel

doi:10.4028/www.scientific.net/AMR.699.584

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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 699Numerical Algorithm for Pore Size Distribution...

Numerical Algorithm for Pore Size Distribution Characterization of Materials from 3D Images

Abstract:

This paper presents an image based numerical method proposed to obtain information regarding pore structure and organization of pores within materials based on 3D digital image input. The output of the numerical algorithm is a pore size distribution of materials. The algorithm is based on the combination of the two digital image processing algorithms: 1) a medial axis thinning algorithm to obtain 3D skeleton of the pore structure, and 2) the distance transform of an image. The method is tested on simple 2D and 3D microstructures of packed spheres, demonstrating the performance of the proposed method.

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Periodical:

Advanced Materials Research (Volume 699)

Pages:

584-589

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.699.584

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Online since:

May 2013

Authors:

Neven Ukrainczyk, Eduard Koenders, Klaas van Breugel

Keywords:

3D Digital Images, Composite Material, Distance Transform, Pore Structure, Size Distribution, Skeletonization

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References

[1] M.Z. Zhang, Y. He, G. Ye, D.A. Lange, K. van Breugel, Construction and Building Materials Vol. 27 (2012) p.472–481

Google Scholar

[2] M. Koster, J. Hannawald and W. Brameshuber, Comput. Mech. Vol. 37 (2006) pp.163-172.

Google Scholar

[3] C.S. Willson: Network Vol. 300 (2005) pp.44-64.

Google Scholar

[4] F. Meier, F. Hansmartin, and A. Steinfeld: Distribution Vol. 29 (2008) pp.315-326.

Google Scholar

[5] J. Große, B. Dietrich, H. Martin, M. Kind, J. Vicente, and E.H. Hardy: Chemical Engineering & Technology Vol. 31 (2008) pp.307-314.

DOI: 10.1002/ceat.200700403

Google Scholar

[6] J. Toriwaki, H. Yoshida, Fundamentals of Three-Dimensional Digital Image Processing, 1st Edition., Springer, London 2009, XVI, 272 p.95

Google Scholar

[7] S. Tran, L. Shih: Efficient 3D binary image skeletonization, in: Computational Systems Bioinformatics Conference, 2005. Workshops and Poster Abstracts. IEEE , pp.364-372, 8-11 Aug. (2005)

DOI: 10.1109/csbw.2005.57

Google Scholar

[8] J.F. Thovert, J. Salles, and P.M. Adler: J. Microsc. Vol. 170 (1993) p.65.

Google Scholar

[9] T.C. Lee, R.L. Kashyap C.N. Chu: CVGIP: Graphical Models and Image Processing Vol. 56 (1994) p.462 – 478.

DOI: 10.1006/cgip.1994.1042

Google Scholar