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

Abstract

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.

The assessment of moderate acne vulgaris face skin using blood perfusion and hyperspectral imaging–A pilot study

“…Higher homogeneity values show less differences in gray among the pixel set. The homogeneity is maximal when all elements in the image are the same 13 . Contrast (equation 1) and Homogeneity (equation 2) show an approximately inverse correlation with respect to an equivalent distribution in the population of pixel pairs.…”

“…The homogeneity is maximal when all elements in the image are the same. 13 Contrast (equation 1) and Homogeneity (equation 2) show an approximately inverse correlation with respect to an equivalent distribution in the population of pixel pairs. Thus, this means that the homogeneity decreases as the contrast increases, and vice versa.…”

Quantitative Evaluation of the Effectiveness of Chemical Peelings in Reducing Acne Lesions Based on Gray-Level Co-Occurrence Matrix (GLCM)

“…Texture information can be obtained by statistically analyzing two-dimensional images. 15 Contrast is the difference between the highest and lowest values of adjacent sets of pixels. High contrast is usually equated with hard textures, while low contrast is equated with smooth.…”

“…Homogeneity is maximal when all elements in the image are the same (if there is no variation among pixels, then homogeneity is 1). 15 Homogeneity (formula 1) and contrast (formula 2) and GLCM are inversely correlated in terms of an equivalent distribution in the population pairs of pixels. This means that the homogeneity decreases as the contrast increases, and vice versa.…”

Non‐invasive assessment of cellulitis from snapshot hyperspectral imaging – A primary study

“…Hyperspectral imaging has been used to evaluate various skin disorders, including aging process and malignant melanoma. 10 In patients with cellulitis, it is sometimes difficult to clearly demarcate a lesion. It would be even more challenging in patients with skin hyperpigmentation or co-existing skin conditions.…”