An Analysis of Human Dorsal Hand Skin Texture Using Hyperspectral Imaging Technique for Assessing the Skin Aging Process

Skin texture has become an important issue in recent research with applications in the cosmetic industry and medicine. In this paper, we analyzed the dependence of skin texture features on wavelength as well as on different parameters (age and gender) of human participants using grey-level co-occurrence matrix and hyperspectral imaging technique for a more accurate quantitative assessment of the aging process. A total of 42 healthy participants (men and women; age range, 20–70 years) was enrolled in this study. A region of interest was selected from the hyperspectral images. The results were analyzed in terms of texture using the gray-level co-occurrence matrix which generated four features (homogeneity, contrast, entropy, and correlation). The results showed that most of these features displayed variations with wavelength (the exception was entropy), with higher variations in women. Only correlation in both sexes and contrast in men proved to vary statistically significant with age, making them the targeted variables in future attempts to characterize aging skin using the complex method of hyperspectral imaging. In conclusion, by using hyperspectral imaging some measure of the degree of damage or the aging process of the hand skin can be obtained, mainly in terms of correlation values. At the present time, reasonable explanations that can link the process of skin aging and the above mentioned features could not be found, but deeper investigations are on the way.