Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-20T08:49:40.577Z Has data issue: false hasContentIssue false
  • English
  • Français

Three-Dimensional Analysis of High-Resolution X-Ray Computed Tomography Data with Morpho+

Published online by Cambridge University Press:  31 January 2011

Loes Brabant ,
Jelle Vlassenbroeck ,
Yoni De Witte ,
Veerle Cnudde ,
Matthieu N. Boone ,
Jan Dewanckele  and
Luc Van Hoorebeke
Loes Brabant*
Affiliation:
Department of Physics and Astronomy, Ghent University, Proeftuinstraat 86, B-9000 Ghent, Belgium
Jelle Vlassenbroeck
Affiliation:
inCT, IIC UGent, Technologiepark 3, B-9052 Ghent, Belgium
Yoni De Witte
Affiliation:
Department of Physics and Astronomy, Ghent University, Proeftuinstraat 86, B-9000 Ghent, Belgium
Veerle Cnudde
Affiliation:
Department of Geology and Soil Science, Ghent University, Krijgslaan 281/S8, B-9000 Ghent, Belgium
Matthieu N. Boone
Affiliation:
Department of Physics and Astronomy, Ghent University, Proeftuinstraat 86, B-9000 Ghent, Belgium
Jan Dewanckele
Affiliation:
Department of Geology and Soil Science, Ghent University, Krijgslaan 281/S8, B-9000 Ghent, Belgium
Luc Van Hoorebeke
Affiliation:
Department of Physics and Astronomy, Ghent University, Proeftuinstraat 86, B-9000 Ghent, Belgium
*
Corresponding author. E-mail: [email protected]
Get access

Abstract

Three-dimensional (3D) analysis is an essential tool to obtain quantitative results from 3D datasets. Considerable progress has been made in 3D imaging techniques, resulting in a growing need for more flexible, complete analysis packages containing advanced algorithms. At the Centre for X-ray Tomography of the Ghent University (UGCT), research is being done on the improvement of both hardware and software for high-resolution X-ray computed tomography (CT). UGCT collaborates with research groups from different disciplines, each having specific needs. To meet these requirements the analysis software package, Morpho+, was developed in-house. Morpho+ contains an extensive set of high-performance 3D operations to obtain object segmentation, separation, and parameterization (orientation, maximum opening, equivalent diameter, sphericity, connectivity, etc.), or to extract a 3D geometrical representation (surface mesh or skeleton) for further modeling. These algorithms have a relatively short processing time when analyzing large datasets. Additionally, Morpho+ is equipped with an interactive and intuitive user interface in which the results are visualized. The package allows scientists from various fields to obtain the necessary quantitative results when applying high-resolution X-ray CT as a research tool to the nondestructive investigation of the microstructure of materials.

Keywords

3D analysisX-ray computed tomography (CT)image analysis3D parametersimagingMorpho+high resolution
Type
Material Applications
Information
Microscopy and Microanalysis , Volume 17 , Issue 2 , April 2011 , pp. 252 - 263
Copyright
Copyright © Microscopy Society of America 2011

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

REFERENCES

Arns, C.H., Knackstedt, M.A., Pinczewske, W.V. & Mecke, K.R. (2001). Euler-Poincare characteristics of classes of disordered media. Phys Rev E 63, 31112.Google Scholar
Béchet, E., Cuillere, J.C. & Trochu, F. (2002). Generation of a finite element MESH from stereolithography (STL) files. Comput Aided Des 34, 1–17.CrossRefGoogle Scholar
Block, A., Von Bloh, W. & Schellnhuber, H.J. (1990). Efficient box-counting determination of generalized fractal dimensions. Phys Rev A 42, 1869–1874.CrossRefGoogle ScholarPubMed
Brun, F., Mancini, L., Kasae, P., Favretto, S., Dreossi, D. & Tromba, G. (2010). Pore3D: A software library for quantitative analysis of porous media. Nucl Instrum Meth A 615, 326–332.Google Scholar
Cnudde, V., Cnudde, J.P., Dupuis, C. & Jacobs, P. (2004). X-ray micro-CT used for the localization of water repellents and consolidants inside natural building stones. Mater Charact 53, 259–271.Google Scholar
Cnudde, V., Silversmit, G., Boone, M., Dewanckele, J., De Samber, B., Schoonjans, T., Van Loo, D., De Witte, Y., Elburg, M., Vincze, L., Van Hoorebeke, L. & Jacobs, P. (2009). Multi-disciplinary characterisation of a sandstone surface crust. Sci Total Environ 407, 5417–5427.Google Scholar
Cornea, N.D., Silver, D. & Min, P. (2007). Curve-skeleton properties, applications, and algorithms. IEEE Trans Vis Comput Graph 13, 530–548.Google Scholar
Dewanckele, J., Cnudde, V., Boone, M., Van Loo, D., De Witte, Y., Pieters, K., Vlassenbroeck, J., Dierick, M., Masschaele, B., Van Hoorebeke, L. & Jacobs, P. (2009). Integration of X-ray micro tomography and fluorescence for applications on natural building stones. J Phys: Conf Ser 186, 012082–012084.Google Scholar
Dhondt, S., Vanhaeren, H., Van Loo, D., Cnudde, V. & Inzé, D. (2010). Plant structure visualization by high-resolution X-ray computed tomography. Trends Plant Sci 15, 419–422.CrossRefGoogle ScholarPubMed
Foubert, A. & Henriet, J.P. (2009). Imaging. In Nature and Significance of the Recent Carbonate Mound Record: The Mound Challenger Code, pp. 45–75. Berlin, Heidelberg: Springer-Verlag.CrossRefGoogle Scholar
Fukuma, Y. (2007). On the sectional invariants of polarized manifolds. J Pure Appl Algebra 209, 99–117.CrossRefGoogle Scholar
Guan, W. & Ma, W. (1998). A list-processing approach to compute Voronoi diagrams and the Euclidean Distance Transform. IEEE Trans Pattern Anal Mach Intell 20, 757–761.CrossRefGoogle Scholar
Havimo, M., Rikala, J., Sirvio, J. & Sipi, M. (2009). Tracheid cross-sectional dimensions in Scots pine (Pinus sylvestris)—Distributions and comparison with Norway spruce (Picea abies). Silv Fen 43, 681–688.Google Scholar
Hildebrand, T. & Rüegsegger, P. (1997). A new method for the model-independent assessment of thickness in three-dimensional images. J Micros 185, 67–75.CrossRefGoogle Scholar
Kak, A.C. & Slaney, M. (1988). Principles of Computerize Tomographic Imaging. New York: IEEE Press Inc.Google Scholar
Ketcham, R.A. (2005). Computational methods for quantitative analysis of three-dimensional features in geological specimens. Geosphere 1, 32–41.Google Scholar
Knuth, D.E. (1997). Art of Computer Programming, Volume 1: Fundamental Algorithms (Third Edition). Reading, MA: Addison-Wesley Professional.Google Scholar
Lin, X., Xiang, S. & Gu, Y. (2008). A new approach to compute the Euler Number of 3D image. In Proceedings of IEEE International Conference on Electronics and Applications, Singapore, pp. 1543–1546. New York: Institute of Electrical and Electronics Engineers.Google Scholar
Lindblad, J. (2005). Surface area estimation of digitized 3D objects using weighted local configurations. Image and Vis Comput 23, 111–122.Google Scholar
Lindquist, W.B. (2002). Quantitative analysis of three dimensional X-ray tomographic images. In Proceedings of SPIE on Developments in X-Ray Tomography III, Bonse, U. (Ed.), pp. 103–115. Washington, DC: Society of Photo-Optical Instrumentation Engineers.Google Scholar
Lindquist, W.B., Lee, S.-M., Coker, D.A., Jones, K.W. & Spanne, P. (1996). Medial axis analysis of void structure in three-dimensional tomographic images of porous media. J Geophys Res 101, 8297–8310.Google Scholar
Mandelbrot, B. (1982). The Fractal Geometry of Nature. New York: W. H. Freeman and Co.Google Scholar
Masschaele, B.C., Cnudde, V., Dierick, M., Jacobs, P., Van Hoorebeke, L. & Vlassenbroeck, J. (2007). UGCT: New X-ray radiography and tomography facility. Nucl Instrum Meth A 580, 266–269.Google Scholar
Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Trans Syst Man Cybern 9, 62–66.CrossRefGoogle Scholar
Pirlet, H., Wehrmann, L.M., Brunner, B., Frank, N., Dewanckele, J., Van Rooij, D., Foubert, A., Swennen, R., Naudts, L., Boone, M., Cnudde, V. & Henriet, J.P. (2010). Diagenetic formation of gypsum and dolomite in a cold-water coral mound in the Porcupine Seabight, off Ireland. Sedimentology 57, 786–805.CrossRefGoogle Scholar
Rosenfeld, A. & Pfaltz, J.L. (1966). Sequential operations in digital picture processing. J ACM 13, 471–494.CrossRefGoogle Scholar
Savina, I.N., Cnudde, V., D'Hollander, S., Van Hoorebeke, L., Mattiasson, B., Galaev, I.Y. & Du Prez, F. (2007). Cryogels from poly(2-hydroxyethyl methacrylate): Macroporous, interconnected materials with potential as cell scaffolds. Soft Matter 3, 1176–1184.Google Scholar
Scholz, G., Zauer, M., Van den Bulcke, J., Van Loo, D., Pfriem, A., Van Acker, K. & Militz, H. (2010). Investigation on wax-impregnated wood. Part 2: Study of void spaces filled with air by He pycnometry, Hg intrusion porosimetry, and 3D X-ray imaging. Holzforschung 64, 587–593.Google Scholar
Sleutel, S., Cnudde, V., Masschaele, B., Vlassenbroek, J., Dierick, M., Van Hoorebeke, L., Jacobs, P. & De Neve, S. (2008). Comparison of different nano- and micro-focus X-ray computed tomography set-ups for the visualization of the soil microstructure and soil organic matter. Comput Geosci 34, 931–938.Google Scholar
Soille, P. (1999). Morphological Image Analysis. Berlin, Heidelberg, New York: Springer-Verlag.Google Scholar
Speijer, R.P., Van Loo, D., Masschaele, B., Vlassenbroeck, J., Cnudde, V. & Jacobs, P. (2008). Quantifying foraminiferal growth with high-resolution X-ray computed tomography: New opportunities in foraminiferal ontogeny, phylogeny, and paleoceanographic applications. Geosphere 4, 760–763.Google Scholar
Tomasi, C. & Manduchi, R. (1998). Bilateral filtering for gray and color images. In Proceedings of IEEE 6th International Conference on Computer Vision, India, pp. 836–846. New York: Institute of Electrical and Electronics Engineers.Google Scholar
Van den Bulcke, J., Boone, M., Van Acker, J., Stevens, M. & Van Hoorebeke, L. (2009a). X-ray tomography as a tool for detailed anatomical analysis. Ann For Sci 5, 1–12.Google Scholar
Van den Bulcke, J., Boone, M., Van Acker, J. & Van Hoorebeke, L. (2009b). Three-dimensional X-ray imaging and analysis of fungi on and in wood. Microsc Microanal 15, 395–402.Google Scholar
Verhoeven, E., Siepmann, F., De Beer, T.R.M., Van Loo, D., Van den Mooter, G., Remon, J.P., Siepmann, J. & Vervaet, C. (2009). Modeling drug release from hot-melt extruded mini-matrices with constant and non-constant diffusivities. Eur J Pharm Biopharm 73, 292–301.CrossRefGoogle ScholarPubMed
Vincent, L. & Soille, P. (1991). Watersheds in digital spaces: An efficient algorithm based on immersion simulations. IEEE Trans Pattern Anal Mach Intell 13, 583–598.Google Scholar
Vlassenbroeck, J., Dierick, M., Masschaele, B., Cnudde, V., Van Hoorebeke, L. & Jacobs, P. (2007). Software tools for quantification of X-ray microtomography at the UGCT. Nucl Instrum Meth A 580, 442–445.Google Scholar