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Title: An automated and unbiased grain segmentation method based on directional reflectance microscopy
Authors: Wittwer, Mallory
Gaskey, Bernard
Seita, Matteo
Keywords: Engineering::Materials
Issue Date: 2021
Source: Wittwer, M., Gaskey, B. & Seita, M. (2021). An automated and unbiased grain segmentation method based on directional reflectance microscopy. Materials Characterization, 174, 110978-.
Project: MOE2017-T2-2-119
Journal: Materials Characterization
Abstract: Identifying individual grains from sectioned polycrystalline metals is a foundational task of microstructure analysis. However, traditional grain segmentation methods applied to optical micrographs may suffer from the lack of optical contrast between grains and require the manual selection of adjustable parameters to achieve acceptable segmentation results. We propose an alternative method which takes advantage of a multi-angle optical microscopy technique termed directional reflectance microscopy. By combining dimensionality reduction, similar-dissimilar classification, and multi-region merging of surface directional reflectance, our method enables fully automated and reliable grain segmentation of polycrystalline surfaces. We apply our method to metal samples with different crystal structures and grain orientation distributions. Our results suggest applicability of the method to a wide range of microstructures, enabling a more objective, robust, and universal characterization of polycrystalline metals.
ISSN: 1044-5803
DOI: 10.1016/j.matchar.2021.110978
Schools: School of Mechanical and Aerospace Engineering 
School of Materials Science and Engineering 
Rights: © 2021 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (
Fulltext Permission: open
Fulltext Availability: With Fulltext
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