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iSpectra: An Open Source Toolbox For The Analysis of Spectral Images Recorded on Scanning Electron Microscopes

Published online by Cambridge University Press:  13 July 2015

Christian Liebske
Christian Liebske*
Affiliation:
Department of Earth Sciences, Institute of Geochemistry and Petrology, ETH Zürich, Sonneggstrasse 5, 8092 Zürich, Switzerland
*
*Corresponding author. christian.liebske@erdw.ethz.ch
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Abstract

iSpectra is an open source and system-independent toolbox for the analysis of spectral images (SIs) recorded on energy-dispersive spectroscopy (EDS) systems attached to scanning electron microscopes (SEMs). The aim of iSpectra is to assign pixels with similar spectral content to phases, accompanied by cumulative phase spectra with superior counting statistics for quantification. Pixel-to-phase assignment starts with a threshold-based pre-sorting of spectra to create groups of pixels with identical elemental budgets, similar to a method described by van Hoek (2014). Subsequent merging of groups and re-assignments of pixels using elemental or principle component histogram plots enables the user to generate chemically and texturally plausible phase maps. A variety of standard image processing algorithms can be applied to groups of pixels to optimize pixel-to-phase assignments, such as morphology operations to account for overlapping excitation volumes over pixels located at phase boundaries. iSpectra supports batch processing and allows pixel-to-phase assignments to be applied to an unlimited amount of SIs, thus enabling phase mapping of large area samples like petrographic thin sections.

Keywords

energy-dispersive spectroscopyspectral imagingphase mappingscanning electron microscopyimage morphology
Type
Materials Applications and Techniques
Information
Microscopy and Microanalysis , Volume 21 , Issue 4 , August 2015 , pp. 1006 - 1016
Copyright
© Microscopy Society of America 2015 

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References

Bachmann, O., Dungan, M.A. & Lipman, P.W. (2002). The Fish Canyon magma body, San Juan volcanic field, Colorado: Rejuvenation and eruption of an upper-crustal batholith. Journal of Petrology 43, 1469–1503.Google Scholar
Bonnet, N., Simova, E., Lebonvallet, S. & Kaplan, H. (1992). New applications of multivariate statistical analysis in spectroscopy and microscopy. Ultramicroscopy 40, 1–11.Google Scholar
Bright, D.S. (1987). A LISP-based image analysis system with applications to microscopy. J Microsc 148, 51–87.CrossRefGoogle Scholar
Bright, D.S. & Newbury, D.E. (1991). Concentration histogram imaging—A scatter diagram technique for viewing 2 or 3 related images. Anal Chem 63, A243–A250.Google Scholar
Bright, D.S. & Newbury, D.E. (2004). Maximum pixel spectrum: A new tool for detecting and recovering rare, unanticipated features from spectrum imaging cubes. J Microsc 216, 186–196.Google Scholar
Cooper, L.B., Bachmann, O. & Huber, C. (2015). Volatile budgets of volcanoes inferred from textural zonation of S-rich hauüyne. Geology 43, 423–426.Google Scholar
Dougtherty, E.R. (1992). Mathematical Morphology in Image Processing. New York: CRC Press.Google Scholar
Drouin, D., Couture, A.R., Joly, D., Tastet, X., Aimez, V. & Gauvin, R. (2007). CASINO V2.42—A fast and easy-to-use modeling tool for scanning electron microscopy and microanalysis users. Scanning 29, 92–101.Google Scholar
Egerton, R.F., Fiori, C.E., Hunt, J.A., Isaacson, M.S., Kirkland, E.J. & Zaluzec, N.J. (1991). EMSA/MAS standard file format for spectral data exchange. EMSA Bull 21, 35–41.Google Scholar
Friel, J.J. & Lyman, C.E. (2006). X-ray mapping in electron-beam instruments. Microsc Microanal 12, 2–25.Google Scholar
Kotula, P.G., Keenan, M.R. & Michael, J.R. (2003). Automated analysis of SEM X-ray spectral images: A powerful new microanalysis tool. Microsc Microanal 9, 1–17.CrossRefGoogle ScholarPubMed
Lanari, P., Vidal, O., De Andrade, V., Dubacq, B., Lewin, E., Grosch, E.G. & Schwartz, S. (2014). XMapTools: A MATLAB (c)-based program for electron microprobe X-ray image processing and geothermobarometry. Comput Geosci 62, 227–240.Google Scholar
Lipman, P.W. & Bachmann, O. (2015). Ignimbrites to batholiths: Integrating perspectives from geological, geophysical, and geochronological data. Geosphere 11, 705–743.Google Scholar
Lucas, G., Burdet, P., Cantoni, M. & Hebert, C. (2013). Multivariate statistical analysis as a tool for the segmentation of 3D spectral data. Micron 52–53, 49–56.Google Scholar
Maloy, A.K. & Treiman, A.H. (2007). Evaluation of image classification routines for determining modal mineralogy of rocks from X-ray maps. Am Mineral 92, 1781–1788.Google Scholar
Newbury, D.E. (2005). Mistakes encountered during automatic peak identification in low beam energy x-ray microanalysis. Scanning 29, 137–151.Google Scholar
Newbury, D.E. (2007). Misidentification of major constituents by automatic qualitative energy dispersive x-ray microanalysis: A problem that threatens the credibility of the analytical community. Microsc Microanal 11, 545–561.Google Scholar
Maloy, A.K. & Treiman, A.H. (2007). Evaluation of image classification routines for determining modal mineralogy of rocks from X-ray maps. Am Mineral 92, 1781–1788.Google Scholar
Parish, C.M. (2011). Multivariate statistics applications in scanning transmission electron microscopy X-ray spectrum imaging. In Advances in Imaging and Electron Physics, Hawkes, P.W. (ed.), pp. 249–295.Google Scholar
Pret, D., Sammartino, S., Beaufort, D., Meunier, A., Fialin, M. & Michot, L.J. (2010). A new method for quantitative petrography based on image processing of chemical element maps: Part I. Mineral mapping applied to compacted bentonites. Am Mineral 95, 1379–1388.CrossRefGoogle Scholar
Schamber, F.H. (1977). A modification of the least-squares fitting method which provides continuum suppresion. In X-ray Analysis of Environmental Samples, Dzubay, T.G. (ed.), pp. 241–257). Ann Arbor, MI: Ann Arbor Science Publishers.Google Scholar
Statham, P.J. (1977). Deconvolution and background subtraction by least-squares fitting with prefiltering of spectra. Anal Chem 49, 2149–2154.Google Scholar
van Hoek, C. (2014). How to process zillions of spectra from spectral imaging datasets? From phase mapping to bulk-chemistry on micron- to centimeter scale using PARC. Microsc Microanal 20(S3), 660–661.Google Scholar
van Hoek, C.J.G., de Roo, M., van der Veer, G. & van der Laan, S.R. (2011). A SEM-EDS study of cultural heritage objects with interpretation of constituents and their distribution using PARC data analysis. Microsc Microanal 17, 656–660.Google Scholar