Beam-hardening correction by a surface fitting and phase classification by a least square support vector machine approach for tomography images of geological samples

Publikationstyp:
Zeitschriftenaufsatz
Metadaten:
Autoren
Sammlungen
  • metadata
ISSN
1869-9537
Ausgabe der Veröffentlichung
4
Zeitschrift
Solid earth discussions
Schlüsselwörter
  • 550 Geowissenschaften
  • 550 Earth sciences
Sprache
eng
Paginierung
Seiten: 3383 - 3408
Datum der Veröffentlichung
2015
Herausgeber
Copernicus Publ.
Herausgeber URL
http://dx.doi.org/10.5194/sed-7-3383-2015
Datum der Datenerfassung
2020
Datum, an dem der Datensatz öffentlich gemacht wurde
2020
Zugang
Public
Titel
Beam-hardening correction by a surface fitting and phase classification by a least square support vector machine approach for tomography images of geological samples
Ausgabe der Zeitschrift
7

Datenquelle: METADATA.UB

Andere Metadatenquellen:
Abstract
<jats:p>Abstract. In X-ray computed microtomography (μXCT) image processing is the most important operation prior to image analysis. Such processing mainly involves artefact reduction and image segmentation. We propose a new two-stage post-reconstruction procedure of an image of a geological rock core obtained by polychromatic cone-beam μXCT technology. In the first stage, the beam-hardening (BH) is removed 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. The final BH-corrected image is extracted from the residual data, or the difference between the surface elevation values and the original grey-scale values. For the second stage, we propose using a least square support vector machine (a non-linear classifier algorithm) to segment the BH-corrected data as a pixel-based multi-classification task. A combination of the two approaches was used to classify a complex multi-mineral rock sample. The Matlab code for this approach is provided in the Appendix. A minor drawback is that the proposed segmentation algorithm may become computationally demanding in the case of a high dimensional training data set. </jats:p>
Autoren
DOI
10.5194/sed-7-3383-2015
Datum der Veröffentlichung
2015
Status
Published
Herausgeber URL
http://dx.doi.org/10.5194/sed-7-3383-2015
Datum der Datenerfassung
2021
Titel
Beam-hardening correction by a surface fitting and phase classification by a least square support vector machine approach for tomography images of geological samples

Datenquelle: Crossref

Author's licence
CC-BY
Autoren
Hosting institution
Universitätsbibliothek Mainz
Sammlungen
  • DFG-OA-Publizieren (2012 - 2017)
Resource version
Published version
DOI
10.5194/sed-7-3383-2015
Funding acknowledgements
  • DFG, Open Access-Publizieren Universität Mainz / Universitätsmedizin
File(s) embargoed
false
Open access
true
ISSN
1869-9537
Ausgabe der Veröffentlichung
4
Zeitschrift
Solid earth discussions
Schlüsselwörter
  • 550 Geowissenschaften
  • 550 Earth sciences
Sprache
eng
Open access status
Open Access
Paginierung
3383 - 3408
Datum der Veröffentlichung
2015
Public URL
https://openscience.ub.uni-mainz.de/handle/20.500.12030/7385
Herausgeber
Copernicus Publ.
Herausgeber URL
http://dx.doi.org/10.5194/sed-7-3383-2015
Datum der Datenerfassung
2022
Datum, an dem der Datensatz öffentlich gemacht wurde
2022
Zugang
Public
Titel
Beam-hardening correction by a surface fitting and phase classification by a least square support vector machine approach for tomography images of geological samples
Ausgabe der Zeitschrift
7

Files

beamhardening_correction_by_a-20220710222815614.pdf

Datenquelle: OPENSCIENCE.UB

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