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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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