Automated analysis of scanning electron microscopic images for assessment of hair surface damage

Chu, Fanny; Anex, Deon S.; Jones, A. Daniel; Hart, Bradley R.

doi:10.1098/rsos.191438

Title: Automated analysis of scanning electron microscopic images for assessment of hair surface damage

Journal Article · 15 January 2020 · Royal Society Open Science

DOI: https://doi.org/10.1098/rsos.191438 · OSTI ID:1557395

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Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA 94550, USA; Department of Chemistry, Michigan State University, 578 S Shaw Ln, East Lansing, MI 48824, USA
Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA 94550, USA
Department of Biochemistry and Molecular Biology, Michigan State University, 603 Wilson Road, East Lansing, MI 48824, USA

Mechanical damage of hair can serve as an indicator of health status and its assessment relies on the measurement of morphological features via microscopic analysis, yet few studies have categorized the extent of damage sustained, and instead have depended on qualitative profiling based on the presence or absence of specific features. We describe the development and application of a novel quantitative measure for scoring hair surface damage in scanning electron microscopic (SEM) images without predefined features, and automation of image analysis for characterization of morphological hair damage after exposure to an explosive blast. Application of an automated normalization procedure for SEM images revealed features indicative of contact with materials in an explosive device and characteristic of heat damage, though many were similar to features from physical and chemical weathering. Assessment of hair damage with tailing factor, a measure of asymmetry in pixel brightness histograms and proxy for surface roughness, yielded 81% classification accuracy to an existing damage classification system, indicating good agreement between the two metrics. Further ability of the tailing factor to score features of hair damage reflecting explosion conditions demonstrates the broad applicability of the metric to assess damage to hairs containing a diverse set of morphological features.

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Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1557395

Report Number(s):: LLNL-JRNL--774282; 966888

Journal Information:: Royal Society Open Science, Journal Name: Royal Society Open Science Journal Issue: 1 Vol. 7; ISSN 2054-5703

Publisher:: The Royal Society PublishingCopyright Statement

Country of Publication:: United States

Language:: English

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Biological and medical sciences
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Supplementary Figures S1 & S2 and Appendices 1 & 2 from Automated analysis of scanning electron microscopic images for assessment of hair surface damage. Chu, Fanny; Anex, Deon S.; Jones, A. Daniel https://doi.org/10.6084/m9.figshare.11397735 The current record is supplemented by this text	text	January 2020
Supplementary Figures S1 & S2 and Appendices 1 & 2 from Automated analysis of scanning electron microscopic images for assessment of hair surface damage Chu, Fanny; Anex, Deon S.; Jones, A. Daniel https://doi.org/10.6084/m9.figshare.11397735.v1 The current record is supplemented by this text	text	January 2019
Supplementary Figures S1 & S2 and Appendices 1 & 2 from Automated analysis of scanning electron microscopic images for assessment of hair surface damage Chu, Fanny; Anex, Deon S.; Jones, A. Daniel https://doi.org/10.6084/m9.figshare.11533254 The current record is supplemented by this text	text	January 2020
Supplementary Figures S1 & S2 and Appendices 1 & 2 from Automated analysis of scanning electron microscopic images for assessment of hair surface damage Chu, Fanny; Anex, Deon S.; Jones, A. Daniel https://doi.org/10.6084/m9.figshare.11533254.v1 The current record is supplemented by this text	text	January 2020
Supplementary material from "Automated analysis of scanning electron microscopic images for assessment of hair surface damage" Chu, Fanny; Anex, Deon S.; Jones, A. Daniel https://doi.org/10.6084/m9.figshare.c.4788729 The current record is supplemented by this collection	collection	January 2019
Supplementary material from "Automated analysis of scanning electron microscopic images for assessment of hair surface damage" Chu, Fanny; Anex, Deon S.; Jones, A. Daniel https://doi.org/10.6084/m9.figshare.c.4788729.v1 The current record is supplemented by this collection	collection	January 2019
Supplementary material from "Automated analysis of scanning electron microscopic images for assessment of hair surface damage" Chu, Fanny; Anex, Deon S.; Jones, A. Daniel https://doi.org/10.6084/m9.figshare.c.4788729.v2 The current record is supplemented by this collection	collection	January 2020
Supplementary material from "Automated analysis of scanning electron microscopic images for assessment of hair surface damage" Chu, Fanny; Anex, Deon S.; Jones, A. Daniel https://doi.org/10.6084/m9.figshare.c.4788729.v3 The current record is supplemented by this collection	collection	January 2020

Title: Automated analysis of scanning electron microscopic images for assessment of hair surface damage

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