Assessment of skin aging grading based on computer vision_Journal of Biomedical Engineering

Authors：

LI Lingyu , XUE Jinxia ,  HE Xiangqian , ZHANG Sheng , FAN Chu

College of Medical Informatics, Chongqing Medical University, Chongqing 400016, P.R.China;

Corresponding author：

HE Xiangqian, Email: hexiangqian@cqmu.edu.cn

Keywords：

image processing; skin aging; self-organizing map network; feature extraction

DOI：

10.7507/1001-5515.201604042

Video：

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Skin aging is the most intuitive and obvious sign of the human aging processes. Qualitative and quantitative determination of skin aging is of particular importance for the evaluation of human aging and anti-aging treatment effects. To solve the problem of subjectivity of conventional skin aging grading methods, the self-organizing map (SOM) network was used to explore an automatic method for skin aging grading. First, the ventral forearm skin images were obtained by a portable digital microscope and two texture parameters, i.e., mean width of skin furrows and the number of intersections were extracted by image processing algorithm. Then, the values of texture parameters were taken as inputs of SOM network to train the network. The experimental results showed that the network achieved an overall accuracy of 80.8%, compared with the aging grading results by human graders. The designed method appeared to be rapid and objective, which can be used for quantitative analysis of skin images, and automatic assessment of skin aging grading.

Citation： LI Lingyu, XUE Jinxia, HE Xiangqian, ZHANG Sheng, FAN Chu. Assessment of skin aging grading based on computer vision. Journal of Biomedical Engineering, 2017, 34(3): 449-455. doi: 10.7507/1001-5515.201604042 Copy

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1. Makrantonaki E, Bekou V, Zouboulis C C. Genetics and skin aging. Dermatoendocrinol, 2012, 4(3): 280-284.
2. Fisher G J, Kang S, Varani J, et al. Mechanisms of photoaging and chronological skin aging. Arch Dermatol, 2002, 138(11): 1462-1470.
3. Rittié L, Fisher G J. Natural and sun-induced aging of human skin. Cold Spring Harb Perspect Med, 2015, 5(1): a015370.
4. 丁克祥. 老化皮肤测定的原理及方法. 国外医学: 老年医学分册, 1999, 20(6): 267-273.
5. 邹耀斌. 皮肤老化分析中的皮沟检测方法研究. 计算机科学与应用, 2012, 2: 32-39.
6. Setaro M, Sparavigna A. Irregularity skin index (ISI): a tool to evaluate skin surface texture. Skin Res Technol, 2001, 7(3): 159-163.
7. Hashimoto K. New methods for surface ultrastructure: Comparative studies of scanning electron microscopy, transmission electron microscopy and replica method. Int J Dermatol, 1975, 13(6): 357-381.
8. Seddon J M, Egan K M, Zhang Y, et al. Evaluation of skin microtopography as a measure of ultraviolet exposure. Invest Ophthalmol Vis Sci, 1992, 33(6): 1903-1908.
9. 王小川. MATLAB 神经网络 43 个案例分析. 北京: 北京航空航天大学出版社, 2013.
10. Trojahn C, Dobos G, Schario M, et al. Relation between skin micro-topography, roughness, and skin age. Skin Res Technol, 2015, 21(1): 69-75.
11. Zou Yaobin, Song Enmin, Jin Renchao. Age-dependent changes in skin surface assessed by a novel two-dimensional image analysis. Skin Res Technol, 2009, 15(4): 399-406.
12. de Paepe K, Lagarde J M, Gall Y, et al. Microrelief of the skin using a light transmission method. Arch Dermatol Res, 2000, 292(10): 500-510.
13. 冈萨雷斯, 伍兹, 埃丁斯, 等. 数字图像处理: MATLAB. 北京: 电子工业出版社, 2005.
14. Zuiderveld K. Contrast limited adaptive histogram equalization// Graphics gems IV. Academic Press Professional, Inc., 1994: 474-485.
15. Noboyuki O. A threshold selection method from gray-level histogram. IEEE Trans Syst Man Cybern Cybern, 1979, 9(1): 62-66.
16. 张东, 覃凤清, 曹磊, 等. 基于中值滤波的椒盐含噪图像去噪方法. 宜宾学院学报, 2014, 14(6): 108-110.
17. Gonzalez R C, Woods R E, Eddins S L. 阮秋琦,等译. 数字图像处理. 第 2 版. 北京: 电子工业出版社, 2007.
18. 江正, 贺向前, 孙露, 等. 通过皮肤图像角点密度和皮沟面积判断皮肤发育的研究. 重庆医科大学学报, 2014, 39(10): 1456-1460.
19. 梁文东. 基于 MATLAB 图像处理的指纹特征点提取. 计算机光盘软件与应用, 2014, 17(4): 24-26.
20. Brighenti C, Sanz-Bobi M Á. Auto-regressive processes explained by self-organized maps. Application to the detection of abnormal behavior in industrial processes. IEEE Trans Neural Netw, 2011, 22(12): 2078-2090.

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Assessment of skin aging grading based on computer vision

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