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

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          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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          Most cited references31

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          NIH Image to ImageJ: 25 years of image analysis.

          Lynda C. Schneider, Wayne S Rasband (2013)
          For the past 25 years NIH Image and ImageJ software have been pioneers as open tools for the analysis of scientific images. We discuss the origins, challenges and solutions of these two programs, and how their history can serve to advise and inform other software projects.
          Article 0 comments Cited 4147 times – based on 0 reviews     Review now
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            ImageJ for microscopy.

            Tony Collins (2007)
            ImageJ is an essential tool for us that fulfills most of our routine image processing and analysis requirements. The near-comprehensive range of import filters that allow easy access to image and meta-data, a broad suite processing and analysis routine, and enthusiastic support from a friendly mailing list are invaluable for all microscopy labs and facilities-not just those on a budget.
            Article 0 comments Cited 515 times – based on 0 reviews     Review now
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              Experimental investigation of collagen waviness and orientation in the arterial adventitia using confocal laser scanning microscopy.

              R Rezakhaniha, A Agianniotis, J T C Schrauwen … (2012)
              Mechanical properties of the adventitia are largely determined by the organization of collagen fibers. Measurements on the waviness and orientation of collagen, particularly at the zero-stress state, are necessary to relate the structural organization of collagen to the mechanical response of the adventitia. Using the fluorescence collagen marker CNA38-OG488 and confocal laser scanning microscopy, we imaged collagen fibers in the adventitia of rabbit common carotid arteries ex vivo. The arteries were cut open along their longitudinal axes to get the zero-stress state. We used semi-manual and automatic techniques to measure parameters related to the waviness and orientation of fibers. Our results showed that the straightness parameter (defined as the ratio between the distances of endpoints of a fiber to its length) was distributed with a beta distribution (mean value 0.72, variance 0.028) and did not depend on the mean angle orientation of fibers. Local angular density distributions revealed four axially symmetric families of fibers with mean directions of 0°, 90°, 43° and -43°, with respect to the axial direction of the artery, and corresponding circular standard deviations of 40°, 47°, 37° and 37°. The distribution of local orientations was shifted to the circumferential direction when measured in arteries at the zero-load state (intact), as compared to arteries at the zero-stress state (cut-open). Information on collagen fiber waviness and orientation, such as obtained in this study, could be used to develop structural models of the adventitia, providing better means for analyzing and understanding the mechanical properties of vascular wall.
              Article 0 comments Cited 289 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Dmitry S. Bulgarevich: bulgarevich@fir.u-fukui.ac.jp
                Journal
                Journal ID (nlm-ta): Sci Rep
                Journal ID (iso-abbrev): Sci Rep
                Title: Scientific Reports
                Publisher: Nature Publishing Group UK (London )
                ISSN (Electronic): 2045-2322
                Publication date (Electronic): 1 February 2018
                Publication date PMC-release: 1 February 2018
                Publication date Collection: 2018
                Volume: 8
                Electronic Location Identifier: 2078
                Affiliations
                [1 ]ISNI 0000 0001 0789 6880, GRID grid.21941.3f, Research and Services Division of Materials Data and Integrated System, , National Institute for Materials Science, ; 1-2-1 Sengen, Tsukuba, Ibaraki 305-0047 Japan
                [2 ]ISNI 0000 0001 0692 8246, GRID grid.163577.1, Research Center for Development of Far-Infrared Region, , University of Fukui, ; Fukui, 3-9-1, Bunkyo, 910-8507 Japan
                [3 ]ISNI 0000 0001 2151 536X, GRID grid.26999.3d, School of Engineering, , The University of Tokyo, ; 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8656 Japan
                Article
                Publisher ID: 20438
                DOI: 10.1038/s41598-018-20438-6
                PMC ID: 5794901
                PubMed ID: 29391483
                SO-VID: f054f1dc-b293-4dfc-8e92-a20cb4f9cc7f
                Copyright © © The Author(s) 2018
                License:

                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/.

                History
                Date received : 23 August 2017
                Date accepted : 18 January 2018
                Categories
                Subject: Article
                Custom metadata
                issue-copyright-statement © The Author(s) 2018

                ScienceOpen disciplines: Uncategorized
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