한국항행학회논문지 (Journal of Advanced Navigation Technology)

  • 제24권2호
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  • Pages.148-154
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  • 2020
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  • 1226-9026(pISSN)
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  • 2288-842X(eISSN)
한국항행학회 (The Korean Navigation Institute)
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피부진단을 위한 딥러닝 기반 피부 영상에서의 자동 주름 추출

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

  • 최현영 (금오공과대학교 ICT융합특성화연구센터) ;
  • 고재필 (금오공과대학교 컴퓨터공학과)
  • Choi, Hyeon-yeong (ICT-CRC, Kumoh National Institute of Technology) ;
  • Ko, Jae-pil (Department of Computer Engineering, Kumoh National Institute of Technology)
  • 투고 : 2020.03.13
  • 심사 : 2020.04.24
  • 발행 : 2020.04.30
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초록

주름은 피부의 노화도를 알 수 있는 주요한 특징 중의 하나이다. 기존의 영상처리기반 주름검출은 다양한 피부 영상에 효과적으로 대처하기 어렵다. 특히, 주름이 선명하지 않고 주변 피부와 유사한 경우 주름추출 성능은 급격히 떨어진다. 본 논문에서는 현미경 피부 영상에서 주름추출을 위해 딥러닝을 적용한다. 일반적으로 현미경 영상은 광각렌즈를 탑재하므로 영상 가장자리 영역의 밝기가 어둡다. 본 논문에서는 이를 해결하기 위해 피부 영상의 밝기를 추정하여 보정 한다. 또한, 주름추출에 적합한 의미분할 네트워크의 구조를 적용한다. 제안방법은 연구실에서 수집한 피부 영상에 대한 테스트 실험에서 99.6%의 정확도를 획득하였다.

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.

키워드

참고문헌

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