Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Application of machine learning techniques in mineral phase segmentation for X-ray microcomputed tomography (µCT) data
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.ORCID iD: 0000-0002-8693-1054
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Geosciences and Environmental Engineering.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.ORCID iD: 0000-0002-5228-3888
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Minerals and Metallurgical Engineering.
Show others and affiliations
2019 (English)In: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 142, article id 105882Article in journal (Refereed) Published
Abstract [en]

X-ray microcomputed tomography (µCT) offers a non-destructive three-dimensional analysis of ores but its application in mineralogical analysis and mineral segmentation is relatively limited. In this study, the application of machine learning techniques for segmenting mineral phases in a µCT dataset is presented. Various techniques were implemented, including unsupervised classification as well as grayscale-based and feature-based supervised classification. A feature matching method was used to register the back-scattered electron (BSE) mineral map to its corresponding µCT slice, allowing automatic annotation of minerals in the µCT slice to create training data for the classifiers. Unsupervised classification produced satisfactory results in terms of segmenting between amphibole, plagioclase, and sulfide phases. However, the technique was not able to differentiate between sulfide phases in the case of chalcopyrite and pyrite. Using supervised classification, around 50–60% of the chalcopyrite and 97–99% of pyrite were correctly identified. Feature based classification was found to have a poorer sensitivity to chalcopyrite, but produced a better result in segmenting between the mineral grains, as it operates based on voxel regions instead of individual voxels. The mineralogical results from the 3D µCT data showed considerable difference compared to the BSE mineral map, indicating stereological error exhibited in the latter analysis. The main limitation of this approach lies in the dataset itself, in which there was a significant overlap in grayscale values between chalcopyrite and pyrite, therefore highly limiting the classifier accuracy.

Place, publisher, year, edition, pages
Elsevier, 2019. Vol. 142, article id 105882
Keywords [en]
X-ray micro-tomography (µCT), Machine learning, Mineral segmentation, Feature-based classification, Feature matching
National Category
Metallurgy and Metallic Materials Geology
Research subject
Mineral Processing; Ore Geology
Identifiers
URN: urn:nbn:se:ltu:diva-75703DOI: 10.1016/j.mineng.2019.105882Scopus ID: 2-s2.0-85070948239OAI: oai:DiVA.org:ltu-75703DiVA, id: diva2:1346176
Note

Validerad;2019;Nivå 2;2019-08-27 (svasva)

Available from: 2019-08-27 Created: 2019-08-27 Last updated: 2019-09-03Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records BETA

Guntoro, Pratama IstiadiTiu, GlacialleGhorbani, YousefLund, CeciliaRosenkranz, Jan

Search in DiVA

By author/editor
Guntoro, Pratama IstiadiTiu, GlacialleGhorbani, YousefLund, CeciliaRosenkranz, Jan
By organisation
Minerals and Metallurgical EngineeringGeosciences and Environmental Engineering
In the same journal
Minerals Engineering
Metallurgy and Metallic MaterialsGeology

Search outside of DiVA

GoogleGoogle Scholar

doi
urn-nbn

Altmetric score

doi
urn-nbn
Total: 32 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf