Automated Analysis of Scanning Electron Microscopic Images for Assessment of Hair Surface Damage

The study developed a novel quantitative measure for assessing hair surface damage using scanning electron microscopic (SEM) images, focusing on automation and characterization of morphological damage after exposure to explosive blasts. The automated analysis used a tailing factor, which measures asymmetry in pixel brightness histograms as a proxy for surface roughness, achieving 81% classification accuracy compared to an existing damage classification system. This method demonstrated the ability to score features of hair damage related to explosion conditions, indicating its broad applicability for assessing diverse morphological features of hair damage.