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

Abstract

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.

Authors:

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Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Michigan State Univ., East Lansing, MI (United States)
Lawrence Livermore National Laboratory, 7000 East Ave., Livermore, CA 94550, USA; Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Michigan State Univ., East Lansing, MI (United States)
Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

Publication Date:: Wed Jan 15 00:00:00 EST 2020

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

Sponsoring Org.:: USDOE National Nuclear Security Administration (NNSA); USDOE Laboratory Directed Research and Development (LDRD) Program

OSTI Identifier:: 1599561

Alternate Identifier(s):: OSTI ID: 1557395

Report Number(s):: LLNL-JRNL-787599; LLNL-JRNL-774282
Journal ID: ISSN 2054-5703; 984952

Grant/Contract Number:: AC52-07NA27344; 16-SI-002

Resource Type:: Accepted Manuscript

Journal Name:: Royal Society Open Science

Additional Journal Information:: Journal Volume: 7; Journal Issue: 1; Journal ID: ISSN 2054-5703

Publisher:: The Royal Society Publishing

Country of Publication:: United States

Language:: English

Subject:: 59 BASIC BIOLOGICAL SCIENCES; 97 MATHEMATICS AND COMPUTING; 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; scanning electron microscopy; hair damage; image analysis; microscopic hair analysis; surface roughness; Biological and medical sciences, Mathematics and Computing, Chemistry - Inorganic, organic, physical and analytical chemistry

Citation Formats


                    Chu, Fanny, Anex, Deon S., Jones, A. Daniel, and Hart, Bradley R. Automated analysis of scanning electron microscopic images for assessment of hair surface damage.  United States: N. p., 2020. 
Web.  doi:10.1098/rsos.191438.

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                    Chu, Fanny, Anex, Deon S., Jones, A. Daniel, & Hart, Bradley R. Automated analysis of scanning electron microscopic images for assessment of hair surface damage.  United States.  https://doi.org/10.1098/rsos.191438

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                    Chu, Fanny, Anex, Deon S., Jones, A. Daniel, and Hart, Bradley R. Wed .  
"Automated analysis of scanning electron microscopic images for assessment of hair surface damage".  United States.  https://doi.org/10.1098/rsos.191438.  https://www.osti.gov/servlets/purl/1599561.

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@article{osti_1599561,

  title        = {Automated analysis of scanning electron microscopic images for assessment of hair surface damage},

  author       = {Chu, Fanny and Anex, Deon S. and Jones, A. Daniel and Hart, Bradley R.},

  abstractNote = {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.},

  doi          = {10.1098/rsos.191438},

  journal      = {Royal Society Open Science},

  number       = 1,

  volume       = 7,

  place        = {United States},

  year         = {Wed Jan 15 00:00:00 EST 2020},

  month        = {Wed Jan 15 00:00:00 EST 2020}

}

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