Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples

Abstract

Image processing of X-ray-computed polychromatic cone-beam micro-tomography (µXCT) data of geological samples mainly involves artefact reduction and phase segmentation. For the former, the main beam-hardening (BH) artefact is removed by applying a best-fit quadratic surface algorithm to a given image data set (reconstructed slice), which minimizes the BH offsets of the attenuation data points from that surface. A Matlab code for this approach is provided in the Appendix. The final BH-corrected image is extracted from the residual data or from the difference between the surface elevation values and the original grey-scale values. For the segmentation, we propose a novel least-squares support vector machine (LS-SVM, an algorithm for pixelbased multi-phase classification) approach. A receiver operating characteristic (ROC) analysis was performed on BHcorrected and uncorrected samples to show that BH correction is in fact an important prerequisite for accurate multiphase classification. The combination of the two approaches was thus used to classify successfully three different more or less complex multi-phase rock core samples.