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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

References

Arganda-Carreras, I, Kaynig, V, Rueden, C, Eliceiri, KW, Schindelin, J, Cardona, A Seung, HS (2017) Trainable Weka segmentation: A machine learning tool for microscopy pixel classification. Bioinformatics 33(15), 2424–2426.Google Scholar
Armienti, P, Francalanci, L Landi, P (2007) Textural effects of steady state behaviour of the Stromboli feeding system. J Volcanol Geotherm Res 160(1–2), 86–98.Google Scholar
Armienti, P, Pareschi, MT, Innocenti, F Pompilio, M (1994) Effects of magma storage and ascent on the kinetics of crystal growth. Contrib Mineral Petrol 115, 402–414.Google Scholar
Auer, A, Martin, CE, Palin, JM, White, JDL, Nakagawa, M Stirling, C (2015) The evolution of hydrous magmas in the Tongariro Volcanic Centre: The 10 ka Pahoka-Mangamate eruptions. New Zeal J Geol Geophys 58(4), 364–384.Google Scholar
Belgiu, M Drăguţ, L (2016) Random forest in remote sensing: A review of applications and future directions. ISPRS J Photogramm Remote Sens 114, 24–31.Google Scholar
Benz, UC, Hofmann, P, Willhauck, G, Lingenfelder, I Heynen, M (2004) Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information. ISPRS J Photogramm Remote Sens 58(3–4), 239–258.Google Scholar
Berger, A, Herwegh, M, Schwarz, J-O Putlitz, B (2011) Quantitative analysis of crystal/grain sizes and their distributions in 2D and 3D. J Struct Geol 33(12), 1751–1763.Google Scholar
Bindeman, IN (2003) Crystal sizes in evolving silicic magma chambers. Geology 31(4), 367–370.Google Scholar
Blaschke, T (2010) Object based image analysis for remote sensing. ISPRS J Photogramm Remote Sens 65(1), 2–16.Google Scholar
Blaschke, T, Lang, S Hay, GJ (2008) Object-Based Image Analysis: Spatial Concepts for Knowledge-Driven Remote Sensing Applications. Berlin: Springer.Google Scholar
Breiman, L (2001) Random forests. Mach Learn 45(1), 5–32.Google Scholar
Brugger, C Hammer, J (2010) Crystal size distribution analysis of plagioclase in experimentally decompressed hydrous rhyodacite magma. Earth Planet Sci Lett 300(3–4), 246–254.Google Scholar
Cashman, KV McConnell, SM (2005) Multiple levels of magma storage during the 1980 summer eruptions of Mount St. Helens, WA. Bull Volcanol 68, 57–75.Google Scholar
Cato, MJ, Fagan, AL Gross, J (2016) Crystal size distribution of low-Ti lunar basalt Northwest-Africa 8632. In 47th Lunar and Planetary Science Conference, The Woodlands, Texas, March 21–25, 2016.Google Scholar
Chardot, L, Jolly, AD, Kennedy, BM, Fournier, N Sherburn, S (2015) Using volcanic tremor for eruption forecasting at White Island volcano (Whakaari), New Zealand. J Volcanol Geotherm Res 302, 11–23.Google Scholar
Chen, X, Zhou, X Wong, STC (2006) Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy. IEEE Trans Biomed Eng 53(4), 762–766.Google Scholar
Cheng, L, Costa, F Carniel, R (2017) Unraveling the presence of multiple plagioclase populations and identification of representative two-dimensional sections using a statistical and numerical approach. Am Mineral 102, 1894–1905.Google Scholar
Crabtree, SM Lange, RA (2010) Complex phenocryst textures and zoning patterns in andesites and dacites: Evidence of degassing-induced rapid crystallization? J Petrol 52(1), 3–38.Google Scholar
Drignon, MJ, Bechon, T, Arbaret, L, Burgisser, A, Komorowski, J, Martel, C, Miller, H Yaputra, R (2016) Preexplosive conduit conditions during the 2010 eruption of Merapi volcano (Java, Indonesia). Geophys Res Lett 43(22), 11595–11602.Google Scholar
Fawcett, T (2006) An introduction to ROC analysis. Pattern Recognit Lett 27, 861–874.Google Scholar
Gutierrez, F Parada, MA (2010) Numerical modeling of time-dependent fluid dynamics and differentiation of a shallow basaltic magma chamber. J Petrol 51(3), 731–762.Google Scholar
Hammer, J, Cashman, KV, Hoblitt, RP Newman, S (1999) Degassing and microlite crystallization during pre-climatic events of the 1991 eruption of Mt. Pinatubo, Philippines. Bull Volcanol 60, 355–380.Google Scholar
Hibert, C, Mangeney, A, Grandjean, G Shapiro, NM (2011) Slope instabilities in Dolomieu crater, Réunion Island: From seismic signals to rockfall characteristics. J Geophy Res 116, F04032.Google Scholar
Higgins, MD (1996a) Crystal size distributions and other quantitative textural measurements in lavas and tuff from Egmont volcano (Mt. Taranaki), New Zealand. Bull Volcanol 58, 194–204.Google Scholar
Higgins, MD (1996b) Magma dynamics beneath Kameni volcano, Thera, Greece, as revealed by crystal size and shape measurements. J Volcanol Geotherm Res 70, 37–48.Google Scholar
Higgins, MD (2000) Measurement of crystal size distributions. Am Mineral 85, 1105–1116.Google Scholar
Higgins, MD (2002) Closure in crystal size distributions (CSD), verification of CSD calculations, and the significance of CSD fans. Am Mineral 87, 171–175.Google Scholar
Higgins, MD Roberge, J (2007) Three magmatic components in the 1973 eruption of Eldfell volcano, Iceland: Evidence from plagioclase crystal size distribution (CSD) and geochemistry. J Volcanol Geotherm Res 161(3), 247–260.Google Scholar
Jerram, DA, Dobson, KJ, Morgan, DJ Pankhurst, MJ (2018) Chapter 8 – The petrogenesis of magmatic systems: Using igneous textures to understand magmatic processes. In: Volcanic and Igneous Plumbing Systems, Burchardt S (Ed.), pp 191–229. Amsterdam: Elsevier.Google Scholar
Jerram, DA Higgins, MD (2007) 3D analysis of rock textures; quantifying igneous microstructures. Elements 3(4), 239–245.Google Scholar
Jerram, DA, Mock, A, Davis, G, Field, M Brown, R (2009) 3D crystal size distributions: A case study on quantifying olivine populations in kimberlites. Lithos 112, 223–235.Google Scholar
Kile, DE Eberl, DD (2003) On the origin of size-dependent and size-independent crystal growth: Influence of advection and diffusion. Am Mineral 88, 1514–1521.Google Scholar
Lentz, RCF McSween, Jr HY (2000) Crystallization of the basaltic shergottites: Insights from crystal size distribution (CSD) analysis of pyroxenes. Meteorit Planet Sci 35(5), 919–927.Google Scholar
Lesher, CE Spera, FJ (2015) Thermodynamic and transport properties of silicate melts and magma. In: The Encyclopedia of Volcanoes, Sigurdsson, H (Ed.), San Diego, CA: Elsevier.Google Scholar
Magee, C, O’Driscoll, B Chambers, AD (2010) Crystallization and textural evolution of a closed-system magma chamber: Insights from a crystal size distribution study of the Lilloise layered intrusion, East Greenland. Geol Mag 147(3), 363–379.Google Scholar
Mahapatra, D (2014) Analyzing training information from random forests for Improved image segmentation. IEEE Transactions on Image Processing 23(4), 1504–1512.Google Scholar
Mangan, MT (1990) Crystal size distribution systematics and the determination of magma storage times: The 1959 eruption of Kilauea volcano, Hawaii. J Volcanol Geotherm Res 44, 295–302.Google Scholar
Marsh, BD (1988) Crystal size distribution (CSD) in rocks and the kinetics and dynamics of crystallisation. I: Theory. Contrib Mineral Petrol 99, 277–291.Google Scholar
Marsh, BD (1998) On the interpretation of crystal size distributions in magmatic systems. J Petrol 39, 553–599.Google Scholar
Meyer, FJ, McAlpin, DB, Gong, W, Ajadi, O, Arko, S, Webley, PW Dehn, J (2015) Integrating SAR and derived products into operational volcano monitoring and decision support systems. ISPRS J Photogramm Remote Sens 100, 106–117.Google Scholar
Mock, A Jerram, DA (2005) Crystal size distributions (CSD) in three dimensions: Insights from the 3D reconstruction of a highly porphyritic rhyolite. J Petrol 46(8), 1525–1541.Google Scholar
Moebis, A (2010) Understanding the Holocene Explosive Eruption Record of the Tongariro Volcanic Centre. PhD thesis, Massey University, Palmerston North.Google Scholar
Morgan, D Jerram, D (2006) On estimating crystal shape for crystal size distribution analysis. J Volcanol Geotherm Res 154(1–2), 1–7.Google Scholar
Morgan, DJ, Jerram, DA, Chertkoff, DG, Davidson, JP, Pearson, DG, Kronz, A Nowell, GM (2007) Combining CSD and isotopic microanalysis: Magma supply and mixing processes at Stromboli Volcano, Aeolian Islands, Italy. Earth Planet Sci Lett 260(3–4), 419–431.Google Scholar
Moss, RA, Hance, GA, Umbaugh, SE Stoecker, WV (1996) Unsupervised color image segmentation: with application to skin tumor borders. IEEE Engineering in Medicine and Biology Magazine, Institute of Electrical and Electronics Engineers (IEEE), Jan 1996.Google Scholar
Muir, DD, Blundy, J Rust, AC (2012) Multiphase petrography of volcanic rocks using element maps: A method applied to Mount St. Helens, 1980–2005. Bull Volcanol 74, 1101–1120.Google Scholar
Pedersen, GBM (2016) Semi-automatic classification of glaciovolcanic landforms: An object-based mapping approach base on geomorphology. J Volcanol Geotherm Res 311, 29–40.Google Scholar
Petrelli, M, Bizzarri, R, Morgavi, D, Baldanza, A Perugini, D (2017) Combining machine learning techniques, microanalyses and large geochemical datasets for tephrochronological studies in complex volcanic areas: New age constraints for the Pleistocene magmatism of central Italy. Quaternary Geochronol 40, 33–44.Google Scholar
Pham, DL, Xu, C Prince, JL (2000) Current methods in medical image segmentation. Ann Rev Biomed Eng 2, 315–337.Google Scholar
Rannou, E Caroff, M (2010) Crystal size distribution in magmatic rocks: Proposition of a synthetic theoretical model. J Petrol 51(5), 1087–1098.Google Scholar
Riggelsen, C Ohrnberger, M (2014) A machine learning approach for improving the detection capabilities at 3C seismic stations. Pure Appl Geophys 171, 395–411.Google Scholar
Sable, JE, Houghton, BF, Wilson, CJN Carey, RJ (2009) Eruption mechanisms during the climax of the Tarawera 1886 basaltic Plinian eruption inferred from microtextural characteristics of the deposits. Studies in Volcanology: The Legacy of George Walker. Geol Soc Lond Spec Publ IAVCEI 2, 129–154.Google Scholar
Simakin, AG Bindeman, IN (2008) Evolution of crystal sizes in the series of dissolution and precipitation events in open magma systems. J Volcanol Geotherm Res 177(4), 997–1010.Google Scholar
Sing, T, Sander, O, Beerenwinkel, N Lengauer, T (2005) ROCR: Visualizing classifier performance in R. Bioinform Appl Note 21(20), 3940–3941.Google Scholar
Szramek, L, Gardner, JE Hort, M (2010) Cooling-induced crystallization of microlite crystals in two basaltic pumice clasts. Am Mineral 95(4), 503–509.Google Scholar
van Rijsbergen, CJ (1979) Information Retrieval. London: Butterworths.Google Scholar
Vasuki, Y, Holden, E-J, Kovesi, P Micklethwaite, S (2017) An interactive image segmentation method for lithological boundary detection: A rapid mapping tool for geologists. Comput Geosci 100, 27–40.Google Scholar
Vona, A Romano, C (2013) The effects of undercooling and deformation rates on the crystallization kinetics of Stromboli and Etna basalts. Contrib Mineral Petrol 166, 491–509.Google Scholar
Witten, IH, Frank, E, Trigg, L, Hall, M Holmes, G (1999) Weka: Practical Machine Learning Tools and Techniques with Java Implementations, Computer Science Working Papers. Hamilton: University of Waikato, Department of Computer Science.Google Scholar