METHODS: We construct a model for skin damage evaluation based on various skin texture features. To accomplish this objective, we consider various features from face, neck, and hand dermoscopy images, including texture length, width and depth, cell area, the number of cells in a fixed region, radius ratio of inscribed and circumscribed circles of a wrinkle cell, and average perimeter of a wrinkle cell. In this study, a wrinkle cell represents the smallest skin region enclosed by textures. We then perform a linear regression for texture features based on subject age.
RESULTS: A dermoscopy image can be automatically analyzed by extracting skin texture features. We demonstrate aging trends by performing linear regression on these features. Based on this result, a quantitative and objective evaluation of the skin condition can be provided.
CONCLUSIONS: We proposed several new skin texture features and developed algorithms to accurately extract them. We analyzed these features and demonstrated their age-related change trends by using graphs and charts. We believe that our result can be used as a standard method for evaluating degrees of skin damage. Moreover, we believe that our proposed method can be applied in various areas, such as performance evaluation of certain skin products.