4.3 Article

Skin texture aging trend analysis using dermoscopy images

期刊

SKIN RESEARCH AND TECHNOLOGY
卷 20, 期 4, 页码 486-497

出版社

WILEY-BLACKWELL
DOI: 10.1111/srt.12143

关键词

skin texture; skin texture analysis; skin feature extraction; dermoscopy image analysis

资金

  1. MSIP(Ministry of Science, ICT and Future Planning), Korea, under the IT/SW Creative research program [NIPA-2013-H0502-13-1119]

向作者/读者索取更多资源

Background/purposeTo date, the degree of skin damage caused by diverse factors, such as aging and persistent sunlight exposure, has been evaluated based on the personal experience and knowledge of dermatologists because there is no standard method for objective evaluation. If a standard method were available, patients could obtain more consistent information about their skin condition, and hence perform more effective treatment of the skin damage. In this paper, we demonstrate how to establish a standard method using dermoscopy images of subjects of various ages. We focus on three body parts, specifically the face, neck, and hands, and extract various skin texture features to quantitatively and objectively represent the skin condition. MethodWe 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. ResultA 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. ConclusionWe 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.

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