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http://dx.doi.org/10.12673/jant.2020.24.2.148

Deep Learning-based Automatic Wrinkles Segmentation on Microscope Skin Images for Skin Diagnosis  

Choi, Hyeon-yeong (ICT-CRC, Kumoh National Institute of Technology)
Ko, Jae-pil (Department of Computer Engineering, Kumoh National Institute of Technology)
Publication Information
Journal of Advanced Navigation Technology / v.24, no.2, 2020 , pp. 148-154 More about this Journal
Abstract
Wrinkles are one of the main features of skin aging. Conventional image processing-based wrinkle detection is difficult to effectively cope with various skin images. In particular, Wrinkle extraction performance is significantly decreased when the wrinkles are not strong and similar to the surrounding skin. In this paper, deep learning is applied to extract wrinkles from microscopic skin images. In general, the microscope image is equipped with a wide-angle lens, so the brightness at the boundary area of the image is dark. In this paper, to solve this problem, the brightness of the skin image is estimated and corrected. In addition, We apply the structure of semantic segmentation network suitable for wrinkle extraction. The proposed method obtained an accuracy of 99.6% in test experiments on skin images collected in our laboratory.
Keywords
Winkles detection; Skin diagnosis; Illumination correction; Convolutional neural networks; Deep learning;
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Times Cited By KSCI : 1  (Citation Analysis)
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