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Description of Ore Particles from X-Ray Microtomography (XMT) Images, Supported by Scanning Electron Microscope (SEM)-Based Image Analysis

Published online by Cambridge University Press:  10 October 2018

Orkun Furat ,
Thomas Leißner ,
Ralf Ditscherlein ,
Ondřej Šedivý ,
Matthias Weber ,
Kai Bachmann ,
Jens Gutzmer ,
Urs Peuker  and
Volker Schmidt
Orkun Furat*
Affiliation:
Institute of Stochastics, Ulm University, D-89069 Ulm, Germany
Thomas Leißner
Affiliation:
Institute of Mechanical Process Engineering and Mineral Processing, Technische Universität Bergakademie Freiberg, D-09599 Freiberg, Germany
Ralf Ditscherlein
Affiliation:
Institute of Mechanical Process Engineering and Mineral Processing, Technische Universität Bergakademie Freiberg, D-09599 Freiberg, Germany
Ondřej Šedivý
Affiliation:
Institute of Stochastics, Ulm University, D-89069 Ulm, Germany
Matthias Weber
Affiliation:
Institute of Stochastics, Ulm University, D-89069 Ulm, Germany
Kai Bachmann
Affiliation:
Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf, D-01328 Dresden, Germany
Jens Gutzmer
Affiliation:
Helmholtz Institute Freiberg for Resource Technology, Helmholtz-Zentrum Dresden-Rossendorf, D-01328 Dresden, Germany
Urs Peuker
Affiliation:
Institute of Mechanical Process Engineering and Mineral Processing, Technische Universität Bergakademie Freiberg, D-09599 Freiberg, Germany
Volker Schmidt
Affiliation:
Institute of Stochastics, Ulm University, D-89069 Ulm, Germany
*
Author for correspondence: Orkun Furat, E-mail: orkun.furat@uni-ulm.de
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Abstract

In this paper, three-dimensional (3D) image data of ore particle systems is investigated. By combining X-ray microtomography with scanning electron microscope (SEM)-based image analysis, additional information about the mineralogical composition from certain planar sections can be gained. For the analysis of tomographic images of particle systems the extraction of single particles is essential. This is performed with a marker-based watershed algorithm and a post-processing step utilizing a neural network to reduce oversegmentation. The results are validated by comparing the 3D particle-wise segmentation empirically with 2D SEM images, which have been obtained with a different imaging process and segmentation algorithm. Finally, a stereological application is shown, in which planar SEM images are embedded into the tomographic 3D image. This allows the estimation of local X-ray attenuation coefficients, which are material-specific quantities, in the entire tomographic image.

Keywords

X-ray microtomography (XMT)mineral liberation analyzer (MLA)segmentationstereologyattenuation coefficient
Type
Materials Science Applications
Information
Microscopy and Microanalysis , Volume 24 , Issue 5 , October 2018 , pp. 461 - 470
Copyright
© Microscopy Society of America 2018 

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References

Anthony, JW, Bideaux, RA, Bladh, KW Nichols, MCN (2004) Handbook of Mineralogy. Chantilly, VA: Mineralogical Society of America.Google Scholar
Bachmann, K, Frenzel, M, Krause, J Gutzmer, J (2017) Advanced identification and quantification of in-bearing minerals by scanning electron microscope-based image analysis. Microsc Microanal 23, 527–537.Google Scholar
Barrett, JF Keat, N (2004) Artifacts in CT: Recognition and avoidance. RadioGraphics 24, 1679–1691.Google Scholar
Boas, FE Fleischmann, D (2012) CT artifacts: Causes and reduction techniques. Imaging Med 4, 229–240.Google Scholar
Buades, A, Coll, B Morel, JM (2005) A non-local algorithm for image denoising. In Computer Society Conference on Computer Vision and Pattern Recognition, CVPR, June 2005, pp. 60–65, San Diego, CA: IEEE Computer Society.Google Scholar
Burger, W Burge, MJ (2010) Digital Image Processing: An Algorithmic Introduction Using Java, 1st ed New York: Springer.Google Scholar
Chiu, SN, Stoyan, D, Kendall, WS Mecke, J (2013) Stochastic Geometry and its Applications, 3rd ed Chichester: Wiley.Google Scholar
Cnudde, V Boone, MN (2013) High-resolution X-ray computed tomography in geosciences: A review of the current technology and applications. Earth Sci Rev 123, 1–17.Google Scholar
Davis, GR Elliott, JC (2006) Artefacts in X-ray microtomography of materials. Mater Sci Technol 22, 1011–1018.Google Scholar
De Boever, W, Derluyn, H, Van Loo, D, Van Hoorebeke, L Cnudde, V (2015) Data-fusion of high resolution X-ray CT, SEM and EDS for 3D and pseudo-3D chemical and structural characterization of sandstone. Micron 74, 15–21.Google Scholar
Fandrich, R, Gu, Y, Burrows, D Moeller, K (2007) Modern SEM-based mineral liberation analysis. Int J Miner Process 84, 310–320.Google Scholar
Gordzins, L (1983) Optimum energies for X-ray transmission tomography of small samples. Applications of synchrotron radiation to computerized tomography I. Nucl Instrum Methods Phys Res 206, 541–545.Google Scholar
Hastie, T, Tibshirani, R Friedman, J (2009) The Elements of Statistical Learning: Data Mining, Inference and Prediction, 2nd ed New York: Springer.Google Scholar
Heinig, T, Bachmann, K, Tolosana-Delgado, R, Van Den Boogaart, G Gutzmer, J (2015) Monitoring gravitational and particle shape settling effects on MLA sampling preparation. In Proceedings of IAMG 2015 - 17th Annual Conference of the International Association for Mathematical Geosciences, September 2015, Freiberg, Germany, pp. 200–206.Google Scholar
Hubbell, JH Seltzer, SM (1995) Tables of X-ray mass attenuation coefficients and mass energy-absorption coefficients 1 keV to 20 MeV for elements Z=1 to 92 and 48 additional substances of dosimetric interest (No. PB-95-220539/XAB; NISTIR-5632). Gaithersburg, MD: National Inst. of Standards and Technology-PL, Ionizing Radiation Div.Google Scholar
Klette, R Rosenfeld, A (2004) Digital Geometry: Geometric Methods for Digital Picture Analysis. Amsterdam: Elsevier.Google Scholar
MacSleyne, JP, Simmons, JP De Graef, M (2008) On the use of moment invariants for the automated analysis of 3D particle shapes. Model Simul Mat Sci Eng 16, 045008.Google Scholar
Maire, E Withers, PJ (2014) Quantitative X-ray tomography. Int Mater Rev 59, 1–43.Google Scholar
Nelder, JA Mead, R (1965) A simplex method for function minimization. Comput J 7, 308–313.Google Scholar
Neumann, M, Cabiscol, R, Osenberg, M, Markötter, H, Manke, I, Finke, J-H Schmidt, V (2018) Characterization of the 3D microstructure of Ibuprofen tablets by means of synchrotron tomography. arXiv preprint: 1806.04631.Google Scholar
Ohser, J Mücklich, F (2000) Statistical Analysis of Microstructures in Materials Science. Chichester: Wiley.Google Scholar
Pavlinsky, GV (2008) Fundamentals of X-Ray Physics. Cambridge: Cambridge International Science Publishing.Google Scholar
Pratt, W (2007) Digital Image Processing, 4th ed Los Altos, CA: Wiley.Google Scholar
Reyes, F, Lin, Q, Udoudo, O, Dodds, C, Lee, PD Neethling, SJ (2017) Calibrated X-ray micro-tomography for mineral ore quantification. Miner Eng 110, 122–130.Google Scholar
Rieder, M, Cavazzini, G, D’Yakonov, YS, Frank-Kamenetskii, VA, Gottardi, G, Guggenheim, S, Koval, PV, Mueller, G, Neiva, AMR, Radoslovich, EW, Robert, JL, Sassi, FP, Takeda, H, Weiss, Z Wones, DR (1998) Nomenclature of the Micas. Can Mineral 36, 905–912.Google Scholar
Roerdink, JBTM Meijster, A (2001) The watershed transform: Definitions, algorithms and parallelization strategies. Fundam Inform 41, 187–228.Google Scholar
Schaeffer, SE (2007) Graph clustering. Comput Sci Rev 1, 27–64.Google Scholar
Schlüter, S, Sheppard, A, Brown, K Wildenschild, D (2014) Image processing of multiphase images obtained via X-ray microtomography: A review. Water Resour Res 50, 3615–3639.Google Scholar
Shafait, F, Keysers, D Breuel, TM (2008) Efficient implementation of local adaptive thresholding techniques using integral images. SPIE Proc 6815, 681510–681516.Google Scholar
Soille, P (2003) Morphological Image Analysis: Principles and Applications, 2nd ed. Berlin: Springer.Google Scholar
Sok, RM, Varslot, T, Ghous, A, Latham, S, Sheppard, AP Knackstedt, MA (2010) Pore scale characterization of carbonates at multiple scales: Integration of Micro-CT, BSEM, and FIBSEM. Petrophysics 51, 379–387.Google Scholar
Spettl, A, Wimmer, R, Werz, T, Heinze, M, Odenbach, S, Krill, CE III Schmidt, V (2015) Stochastic 3D modeling of Ostwald ripening at ultra-high volume fractions of the coarsening phase. Model Simul Mat Sci Eng 23, 065001.Google Scholar
Sunderland, D Gottlieb, P (1991) Application of automated quantitative mineralogy in mineral processing. Miner Eng 4, 753–762.Google Scholar