PhaseQuant: A tool for quantifying tomographic data sets of geological specimens

Micro-CT is becoming an increasingly important tool for non-destructive analysis of rock specimens. One of the major challenges with micro-CT is to extract quantitative information as opposed to qualitative information from the datasets. In this paper, PhaseQuant - a new software tool for processing a micro-CT image stack - is introduced. PhaseQuant is an open source freeware distributed as an ImageJ plugin. PhaseQuant is a simple and easy-to-use software tool that comprises phase segmentation, phase measurement, validation and density calibration modules which together enable the user to follow a repeatable experimentation protocol for quantifying phases and components from a micro-CT image stack of rock specimens. The techniques used in the software tool are outlined in this paper along with some illustrative examples of application of the software to meteorites and rock cores. Detailed instructions on how to use the code are available on the Internet.^4

[1]  Dougal A. Jerram,et al.  The Field Description of Igneous Rocks , 2011 .

[2]  Manuel Dierick,et al.  3D quantification of mineral components and porosity distribution in Westphalian C sandstone by microfocus X-ray computed tomography , 2009 .

[3]  Tony F. Chan,et al.  Image processing and analysis , 2005 .

[4]  Premkumar Elangovan,et al.  Characterisation of gold ores by X-ray computed tomography - Part 2: Applications to the determination of gold particle size and distribution , 2011 .

[5]  Mark L. Rivers,et al.  Pore size distribution in an uncompacted equilibrated ordinary chondrite , 2008 .

[6]  Anil K. Jain Data clustering: 50 years beyond K-means , 2010, Pattern Recognit. Lett..

[7]  Rudy Swennen,et al.  Beam hardening artifact reduction in microfocus computed tomography for improved quantitative coal characterization , 2006 .

[8]  S. L. Wellington,et al.  X-ray computerized tomography , 1987 .

[9]  Mark L. Rivers,et al.  Meteorite 3‐D synchrotron microtomography: Methods and applications , 2007 .

[10]  W. S. Rasband,et al.  ImageJ: Image processing and analysis in Java , 2012 .

[11]  David G. Stork,et al.  Pattern Classification , 1973 .

[12]  F. Cordelières,et al.  A guided tour into subcellular colocalization analysis in light microscopy , 2006, Journal of microscopy.

[13]  Denton S. Ebel,et al.  Shape, metal abundance, chemistry, and origin of chondrules in the Renazzo (CR) chondrite , 2008 .

[14]  R. Ketcham Three-dimensional grain fabric measurements using high-resolution X-ray computed tomography , 2005 .

[15]  Ryan T. Armstrong,et al.  Characterisation of gold ores by X-ray computed tomography - Part 1: Software for calibration and quantification of mineralogical phases , 2011 .

[16]  Michael D. Abràmoff,et al.  Image processing with ImageJ , 2004 .

[17]  Richard A. Ketcham,et al.  Computational methods for quantitative analysis of three-dimensional features in geological specimens , 2005 .

[18]  Martine Wevers,et al.  Towards 3-D petrography: application of microfocus computer tomography in geological science , 2001 .

[19]  Jon M. Friedrich,et al.  Quantitative methods for three-dimensional comparison and petrographic description of chondrites , 2008, Comput. Geosci..

[20]  Premkumar Elangovan,et al.  Improved segmentation of meteorite micro-CT images using local histograms , 2012, Comput. Geosci..