Automatic segmentation of a tongue area and oriental medicine tongue diagnosis system using the learning of the area features

영역 특징 학습을 이용한 혀의 자동 영역 분리 및 한의학적 설진 시스템

  • Received : 2016.02.22
  • Accepted : 2016.03.25
  • Published : 2016.04.30


In this paper, we propose a tongue diagnosis system for determining the presence of specific taste crack area as a first step in the digital tongue diagnosis system that anyone can use easily without special equipment and expensive digital tongue diagnosis equipment. Training DB was developed by the Haar-like feature, Adaboost learning on the basis of 261 pictures which was collected in Oriental medicine. Tongue candidate regions were detected from the input image by the learning results and calculated the average value of the HUE component to separate only the tongue area in the detected candidate regions. A tongue area is separated through the Connected Component Labeling from the contour of tongue detected. The palate regions were divided by the relative width and height of the tongue regions separated. Image on the taste area is converted to gray image and binarized with each of the average brightness values. A crack in the presence or absence was determined via Connected Component Labeling with binary images.


Tongue diagnosis;Color analysis;Haar-like feautre;Adaboost


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Supported by : Daejeon University