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.
Highlights
Digital image analysis has been underutilized for classification of hair fibres from various microscopic methods, despite offering potential for more objective detection and comparison of image features
Single hairs recovered after exposure to explosive blast conditions sustained damage comparable to that from physical and chemical weathering, as similar morphological features were identified in this study
Images were scored based on qualitative presence or absence of features, as described in the scanning electron microscopic (SEM) damage grade system proposed by Kim et al, where overlapped cuticles represent the lowest degree of hair surface damage
Summary
Digital image analysis has been underutilized for classification of hair fibres from various microscopic methods, despite offering potential for more objective detection and comparison of image features. Structural differences between two hair segments (e.g. width, curvature), even along the length of a hair, and automatic setting of brightness and contrast parameters for optimal SEM image acquisition make feature detection and hair segment comparison in image analysis challenging. While many normalization methods have been implemented to remove image artefacts such as brightness variation, procedures used to process digital images focus on contrast enhancement. Disease diagnosis [24,25] and in digital images for facial recognition [26,27] These methods necessitate user inputs and parameter optimization, such as the gamma value in GIC, and are used to enhance features for detection in an image. Metrics to quantitate pixel brightness from these features, including roughness and tailing factor, were evaluated for broadly applicable scoring of hair surface damage
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