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Weka Trainable Segmentation Plugin in ImageJ: A Semi-Automatic Tool Applied to Crystal Size Distributions of Microlites in Volcanic Rocks

Published online by Cambridge University Press:  27 December 2018

Charline Lormand ,
Georg F. Zellmer ,
Károly Németh ,
Geoff Kilgour ,
Stuart Mead ,
Alan S. Palmer ,
Naoya Sakamoto ,
Hisayoshi Yurimoto  and
Anja Moebis
Charline Lormand*
Affiliation:
School of Agriculture and Environment, Volcanic Risk Solutions, Massey University, P.O. Box 11222, Palmerston North 4442, New Zealand
Georg F. Zellmer
Affiliation:
School of Agriculture and Environment, Volcanic Risk Solutions, Massey University, P.O. Box 11222, Palmerston North 4442, New Zealand
Károly Németh
Affiliation:
School of Agriculture and Environment, Volcanic Risk Solutions, Massey University, P.O. Box 11222, Palmerston North 4442, New Zealand
Geoff Kilgour
Affiliation:
GNS Science, Wairakei Research Centre, P.O. Box 2000, Taupo 3352, New Zealand
Stuart Mead
Affiliation:
School of Agriculture and Environment, Volcanic Risk Solutions, Massey University, P.O. Box 11222, Palmerston North 4442, New Zealand
Alan S. Palmer
Affiliation:
Department of Soil and Earth Sciences, School of Agriculture and Environment, Massey University, P.O. Box 11222, Palmerston North 4442, New Zealand
Naoya Sakamoto
Affiliation:
Isotope Imaging Laboratory, Creative Research Institution, Hokkaido University, Sapporo 060-0810, Japan
Hisayoshi Yurimoto
Affiliation:
Isotope Imaging Laboratory, Creative Research Institution, Hokkaido University, Sapporo 060-0810, Japan
Anja Moebis
Affiliation:
Department of Soil and Earth Sciences, School of Agriculture and Environment, Massey University, P.O. Box 11222, Palmerston North 4442, New Zealand
*
*Author for correspondence: Charline Lormand, E-mail: [email protected]
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Abstract

Crystals within volcanic rocks record geochemical and textural signatures during magmatic evolution before eruption. Clues to this magmatic history can be examined using crystal size distribution (CSD) studies. The analysis of CSDs is a standard petrological tool, but laborious due to manual hand-drawing of crystal margins. The trainable Weka segmentation (TWS) plugin in ImageJ is a promising alternative. It uses machine learning and image segmentation to classify an image. We recorded back-scattered electron (BSE) images of three volcanic samples with different crystallinity (35, 50 and ≥85 vol. %), using scanning electron microscopes (SEM) of variable image resolutions, which we then tested using TWS. Crystal measurements obtained from the automatically segmented images are compared with those of the manual segmentation. Samples up to 50 vol. % crystallinity are successfully segmented using TWS. Segmentation at significantly higher crystallinities fails, as crystal boundaries cannot be distinguished. Accuracy performance tests for the TWS classifiers yield high F-scores (>0.930), hence, TWS is a successful and fast computing tool for outlining crystals from BSE images of glassy rocks. Finally, reliable CSD’s can be derived using a low-cost desktop SEM, paving the way for a wide range of research to take advantage of this new petrological method.

Keywords

crystal size distributionsmicroliteselectron microprobeimage segmentationautomation
Type
Software and Instrumentation
Information
Microscopy and Microanalysis , Volume 24 , Issue 6 , December 2018 , pp. 667 - 675
Copyright
© Microscopy Society of America 2018 

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Footnotes

Cite this article: Lormand C, Zellmer GF, Németh K, Kilgour G, Mead S, Palmer AS, Sakamoto N, Yurimoto H and Moebis A (2018) Weka Trainable Segmentation Plugin in ImageJ: A Semi-Automatic Tool Applied to Crystal Size Distributions of Microlites in Volcanic Rocks. Microsc Microanal24(6), 667–675. doi: 10.1017/S1431927618015428

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