A Novel Technique for Producing Three-Dimensional Data Using Serial Sectioning and Semi-Automatic Image Classification

The three-dimensional characterization of internal features, via metrics such as orientation, porosity, and connectivity, is important to a wide variety of scientific questions. Many spatial and morphological metrics only can be measured accurately through direct in situ three-dimensional observations of large (i.e., big enough to be statistically representative) volumes. For samples that lack material contrast between phases, serial grinding and imaging-which relies solely on color and textural characteristics to differentiate features-is a viable option for extracting such information. Here, we present the Grinding, Imaging, Reconstruction Instrument (GIRI), which automatically serially grinds and photographs centimeter-scale samples at micron resolution. Although the technique is destructive, GIRI produces an archival digital image stack. This digital image stack is run through a supervised machine-learning-based image processing technique that quickly and accurately segments data into predefined classes. These classified data then can be loaded into three-dimensional visualization software for measurement. We share three case studies to illustrate how GIRI can address questions with a significant morphological component for which two-dimensional or small-volume three-dimensional measurements are inadequate. The analyzed metrics include: the morphologies of objects and pores in a granular material, the bulk mineralogy of polyminerallic solids, and measurements of the internal angles and symmetry of crystals.