Multi-phase classification by a least-squares support vector machine approach in tomography images of geological samples
Image processing of X-ray-computed polychromatic cone-beam micro-tomography (<i>μ</i>XCT) 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 algori...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-03-01
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| Series: | Solid Earth |
| Online Access: | http://www.solid-earth.net/7/481/2016/se-7-481-2016.pdf |
| Summary: | Image processing of X-ray-computed polychromatic cone-beam micro-tomography
(<i>μ</i>XCT) 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 pixel-based multi-phase classification) approach. A receiver operating
characteristic (ROC) analysis was performed on BH-corrected and uncorrected
samples to show that BH correction is in fact an important prerequisite for
accurate multi-phase classification. The combination of the two approaches
was thus used to classify successfully three different more or less complex
multi-phase rock core samples. |
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| ISSN: | 1869-9510 1869-9529 |