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Image processing of X-ray-computed polychromatic cone-beam micro-tomography(<i>μ</i>XCT) data of geological samples mainly involves artefact reductionand phase segmentation. For the former, the main beam-hardening (BH)artefact is removed by applying a best-fit quadratic surface algorithm to agiven image data set (reconstructed slice), which minimizes the BH offsetsof the attenuation data points from that surface. A Matlab code for thisapproach is provided in the Appendix. The final BH-corrected image isextracted from the residual data or from the difference between the surfaceelevation values and the original grey-scale values. For the segmentation,we propose a novel least-squares support vector machine (LS-SVM, an algorithmfor pixel-based multi-phase classification) approach. A receiver operatingcharacteristic (ROC) analysis was performed on BH-corrected and uncorrectedsamples to show that BH correction is in fact an important prerequisite foraccurate multi-phase classification. The combination of the two approacheswas thus used to classify successfully three different more or less complexmulti-phase rock core samples

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