title: Automatic measurement of skin textures of the dorsal hand in evaluating skin aging.
abstract: OBJECTIVE
  Changes in skin textures have been used to evaluate skin aging in many studies. In our previous study, we built some skin texture parameters, which can be used to evaluate skin aging of human dorsal hand. However, it will take too much time and need to work arduously to get the information from digital skin image by manual work. So, we want to build a simple and effective method to automatically count some of those skin texture parameters by using digital image-processing technology.
  
  
  METHODS
  A total of 100 subjects aged 30 years and above were involved. Sun exposure history and demographic information were collected by using a questionnaire. The skin image of subjects' dorsal hand was obtained by using a portable skin detector. The number of grids, which is one of skin texture parameters built in our previous study, was measured manually and automatically. Automated image analysis program was developed by using Matlab 7.1 software.
  
  
  RESULTS
  The number of grids counted automatically (NGA) was significantly correlated with the number of grids counted manually (NGM) (r = 0.9287, P < 0.0001). And in each age group, there were no significant differences between NGA and NGM. The NGA was negatively correlated with age and lifetime sun exposure, and decreased with increasing Beagley-Gibson score from 3 to 6. In addition, even after adjusting for NGA, the standard deviation of grid areas for each image was positively correlated with age, sun exposure, and Bealey-Gibson score.
  
  
  CONCLUSIONS
  The method introduced in present study can be used to measure some skin aging parameters automatically and objectively. And it will save much time, reduce labor, and avoid measurement errors of deferent investigators when evaluating a great deal of skin images in a short time.
authors: Department of Environmental Health, School of Public Health, China Medical University, Shenyang, China.
authors: Qian
authors: Q
authors: Gao
authors: Jiaming
authors: J
authors: Yu
authors: Fang
authors: F
authors: Wang
authors: Tiantian
authors: T
authors: Ge
authors: Liwen
authors: L
authors: Hu
authors: Yang
authors: Y
authors: Liu
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000375:Aging / Q000473:pathology* / Q000502:physiology*; D000465:Algorithms; D046169:Dermoscopy / Q000379:methods*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012867:Skin / Q000166:cytology*; D015595:Skin Aging / Q000502:physiology*