Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures

For advanced materials characterization, a novel and extremely effective approach of pattern recognition in optical microscopic images of steels is demonstrated. It is based on fast Random Forest statistical algorithm of machine learning for reliable and automated segmentation of typical steel micro...

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Autores principales: Bulgarevich, Dmitry S., Tsukamoto, Susumu, Kasuya, Tadashi, Demura, Masahiko, Watanabe, Makoto
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5794901/
https://www.ncbi.nlm.nih.gov/pubmed/29391483
http://dx.doi.org/10.1038/s41598-018-20438-6
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author Bulgarevich, Dmitry S.
Tsukamoto, Susumu
Kasuya, Tadashi
Demura, Masahiko
Watanabe, Makoto
author_facet Bulgarevich, Dmitry S.
Tsukamoto, Susumu
Kasuya, Tadashi
Demura, Masahiko
Watanabe, Makoto
author_sort Bulgarevich, Dmitry S.
collection PubMed
description For advanced materials characterization, a novel and extremely effective approach of pattern recognition in optical microscopic images of steels is demonstrated. It is based on fast Random Forest statistical algorithm of machine learning for reliable and automated segmentation of typical steel microstructures. Their percentage and location areas excellently agreed between machine learning and manual examination results. The accurate microstructure pattern recognition/segmentation technique in combination with other suitable mathematical methods of image processing and analysis can help to handle the large volumes of image data in a short time for quality control and for the quest of new steels with desirable properties.
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spelling pubmed-57949012018-02-12 Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures Bulgarevich, Dmitry S. Tsukamoto, Susumu Kasuya, Tadashi Demura, Masahiko Watanabe, Makoto Sci Rep Article For advanced materials characterization, a novel and extremely effective approach of pattern recognition in optical microscopic images of steels is demonstrated. It is based on fast Random Forest statistical algorithm of machine learning for reliable and automated segmentation of typical steel microstructures. Their percentage and location areas excellently agreed between machine learning and manual examination results. The accurate microstructure pattern recognition/segmentation technique in combination with other suitable mathematical methods of image processing and analysis can help to handle the large volumes of image data in a short time for quality control and for the quest of new steels with desirable properties. Nature Publishing Group UK 2018-02-01 /pmc/articles/PMC5794901/ /pubmed/29391483 http://dx.doi.org/10.1038/s41598-018-20438-6 Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Bulgarevich, Dmitry S.
Tsukamoto, Susumu
Kasuya, Tadashi
Demura, Masahiko
Watanabe, Makoto
Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures
title Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures
title_full Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures
title_fullStr Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures
title_full_unstemmed Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures
title_short Pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures
title_sort pattern recognition with machine learning on optical microscopy images of typical metallurgical microstructures
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5794901/
https://www.ncbi.nlm.nih.gov/pubmed/29391483
http://dx.doi.org/10.1038/s41598-018-20438-6
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