Automated extraction of symmetric microstructure features in serial sectioning images

Serial sectioning methods continue to produce a wealth of image data for quantifying the three-dimensional nature of material microstructures. In this work, we discuss a computational methodology for automated detection and 3D characterization of dendrite cores from images taken from slices of a production turbine blade made of a heat-treated single crystal Ni-based superalloy. The dendrite core locations are detected using an automated segmentation technique that incorporates information over multiple length scales and exploits the four-fold symmetry of the dendrites when viewed down the 〈100〉 growth direction. Additional rules that take advantage of the continuity of the dendrites from slice to slice help to exclude segmentation artifacts and improve dendrite core segmentation. The significance of this technique is that it may be extended to include any symmetric features.