| Autor: |
Furat O; 1Institute of Stochastics,Ulm University,D-89069 Ulm,Germany., Leißner T; 2Institute of Mechanical Process Engineering and Mineral Processing,Technische Universität Bergakademie Freiberg,D-09599 Freiberg,Germany., Ditscherlein R; 2Institute of Mechanical Process Engineering and Mineral Processing,Technische Universität Bergakademie Freiberg,D-09599 Freiberg,Germany., Šedivý O; 1Institute of Stochastics,Ulm University,D-89069 Ulm,Germany., Weber M; 1Institute of Stochastics,Ulm University,D-89069 Ulm,Germany., Bachmann K; 3Helmholtz Institute Freiberg for Resource Technology,Helmholtz-Zentrum Dresden-Rossendorf,D-01328 Dresden,Germany., Gutzmer J; 3Helmholtz Institute Freiberg for Resource Technology,Helmholtz-Zentrum Dresden-Rossendorf,D-01328 Dresden,Germany., Peuker U; 2Institute of Mechanical Process Engineering and Mineral Processing,Technische Universität Bergakademie Freiberg,D-09599 Freiberg,Germany., Schmidt V; 1Institute of Stochastics,Ulm University,D-89069 Ulm,Germany. |
| Abstrakt: |
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. |
