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Application of machine learning techniques in mineral phase segmentation for X-ray microcomputed tomography (µCT) data
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.ORCID-id: 0000-0002-8693-1054
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Geovetenskap och miljöteknik.
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.ORCID-id: 0000-0002-5228-3888
Luleå tekniska universitet, Institutionen för samhällsbyggnad och naturresurser, Mineralteknik och metallurgi.
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2019 (engelsk)Inngår i: Minerals Engineering, ISSN 0892-6875, E-ISSN 1872-9444, Vol. 142, artikkel-id 105882Artikkel i tidsskrift (Fagfellevurdert) 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.

sted, utgiver, år, opplag, sider
Elsevier, 2019. Vol. 142, artikkel-id 105882
Emneord [en]
X-ray micro-tomography (µCT), Machine learning, Mineral segmentation, Feature-based classification, Feature matching
HSV kategori
Forskningsprogram
Mineralteknik; Malmgeologi
Identifikatorer
URN: urn:nbn:se:ltu:diva-75703DOI: 10.1016/j.mineng.2019.105882ISI: 000488141400014Scopus ID: 2-s2.0-85070948239OAI: oai:DiVA.org:ltu-75703DiVA, id: diva2:1346176
Merknad

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

Tilgjengelig fra: 2019-08-27 Laget: 2019-08-27 Sist oppdatert: 2019-10-18bibliografisk kontrollert

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

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