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Personen: Khan, F. (Autor) 
Enzmann, Frieder (Autor) 
Kersten, Michael (Autor) 
  
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
  
Quelle: Solid earth discussions. Bd. 7. H. 4. Göttingen : Copernicus Publ. S. 3383 - 3408
Erscheinungsjahr:    2015
ISBN / ISSN: 1869-9537
URL der Originalveröffentlichung doi:10.5194/sed-7-3383-2015
  
Dokumentart:
Zeitschriftenaufsatz Zeitschriftenaufsatz
Sprache: Englisch
Open Access: OpenAccess
Person der Universität:    Enzmann, Frieder  In UnivIS suchen ; Kersten, Michael  In UnivIS suchen 
Einrichtung: Institut für Geowissenschaften
DDC-Sachgruppe:    Geowissenschaften
ID: 52629  Universitätsbibliothek Mainz
Hinweis:
Informationen zu den Nutzungsrechten unserer Inhalte Informationen zu den Nutzungsrechten unserer Inhalte
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.
   
  
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