Publications Repository - Helmholtz-Zentrum Dresden-Rossendorf

This paper demonstrates a new method to classify mineral phases in 3D images of particulate materials obtained by x-ray computed tomography. The method consists of 1) sample preparation and scanning of a particle dispersion; 2) image processing optimized towards the labelling of individual particles in the sample; 3) phase identification is done at the particle level using an interpretation of the grey-values of all voxels in a particle rather than of all voxels in the sample. The method allows minimizing the impact of typical image artefacts in the classification of voxels, i.e. beam hardening, partial volume effect, spot blurriness and cone beam. Additionally, the particle’s geometry and microstructure can be used as classification criteria besides the grey-values. The result is an increased accuracy of phase classification, reduced detection limit of phases, lower grain size to voxel size ratio that can be detected, individual particle statistics can be measured instead of only bulk statistics. Consequently, the method broadens the applicability of 3D imaging techniques for particle analysis at low particle size to voxel size ratio, which is typically limited by unreliable phase classification that leads to inaccurate quantification. This opens the possibility for 3D semi-automated mineralogy.