Abstract
Several automated algorithms are presented for the segmentation of features of interest from microstructure images acquired with modern high-throughput electron microscopes. Specifically, the maximization of posterior marginals (MPM) segmentation technique, originally developed for computer vision applications, is applied towards automated segmentation of microstructural images from Ti- and Ni-alloy systems. The MPM technique classifies image pixels according to the most probable class to which they can belong. Three derivatives of the MPM algorithm are introduced and assessed: expectation maximization MPM (EM/MPM), EM/MPM with simulated annealing (EM/MPM/SA) and vector EM/MPM/SA. Example applications of all three approaches are given. The EM/MPM model allows for automated segmentation of α laths in a Ti-6242 sample and primary γ′ in an IN100 superalloy, but has difficulty accurately locating the boundaries between regions. The EM/MPM/SA algorithm involves a gradual increase in the interface capillarity during segmentation and allows for pixel accuracy determination of boundaries between phases. The vector EM/MPM/SA method is capable of simultaneously segmenting a series of images acquired with differing imaging conditions. The limitations of the algorithms are discussed as well as potential future modifications.
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More From: Modelling and Simulation in Materials Science and Engineering
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