基于机器视觉下的皮肤老化分级研究

Assessment of skin aging grading based on computer vision

皮肤老化是人体衰老进程中最明显的标志,对其进行定性或定量评价具有重要意义,并可广泛应用于人体衰老程度研究以及抗衰老措施功效评价等领域。针对传统人为皮肤老化分级的主观性,本文探索用自组织映射(SOM)神经网络实现对皮肤老化程度的自动分级。首先,采用便携式数码显微镜获取人体前臂腹侧皮肤图像,经图像处理分析,提取皮肤纹理参数:皮沟平均宽度和交点个数,用于表征皮肤纹理老化的变化情况;其次,将纹理参数值输入SOM神经网络,用于网络训练学习及分级。结果显示,本文所设计的基于机器视觉下的皮肤老化评价方法,与人工方法相比较,分类一致率达80.8%,可实现较为客观且快速的皮肤老化分级。

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.