Application of machine learning techniques in mineral phase segmentation for X-ray microcomputed tomography (µCT) data

Guntoro, P.I., Tiu, G., Ghorbani, Y. , Lund, C. and Rosenkranz, J. (2019) Application of machine learning techniques in mineral phase segmentation for X-ray microcomputed tomography (µCT) data. Minerals Engineering, 142 . p. 105882.

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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. © 2019 Elsevier Ltd

Keywords:Computerized tomography, Copper compounds, Feldspar, Learning algorithms, Learning systems, Machine learning, Mineral exploration, Pyrites, Sulfur compounds, Supervised learning, Feature-based classification, Machine learning techniques, Supervised classification, Three-dimensional analysis, Unsupervised classification, X ray micro-computed tomography, X-ray micro tomographies, Classification (of information), Feature matching
Divisions:College of Science
College of Science > School of Chemistry
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ID Code:54526
Deposited On:27 Jul 2023 14:33

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