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

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 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.