Comparing Level Set Method and Canny Algorithm for Edge Detection to Tongue Diagnosis in Traditional Chinese Medicine

2012 ◽  
Vol 236-237 ◽  
pp. 783-786
Author(s):  
Yen Sheng Chen ◽  
Chung Hua Chen ◽  
Yuh Ming Chang ◽  
Chun Chih Chang
Keyword(s):  
Health Status ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Level Set Method ◽  
Level Set ◽  
Diagnostic Method ◽  
Diagnostic System ◽  
Tongue Diagnosis ◽  
Human Tongue ◽  
Canny Algorithm

The tongue diagnosis is an important diagnostic method in Traditional Chinese Medicine (TCM). Human tongue is one of the im¬portant organs which contain the information of health status. In order to achieve an automatic tongue diagnostic system, an effective segmentation me¬thod for detecting the edge of tongue is very important. We mainly compare the Level Set Method and Canny algorithm for edge segmentation. The segmentation using Canny algorithm may produce many false edges after cutting; thus, it is not suitable for use. But, Level Set Method can produce better edge contour. Therefore, it may be useful in clinical automated tongue diagnosis system. Experiments show the results of these techniques.

Comparing Intuitionistic Fuzzy Set Theory Method and Canny Algorithm for Edge Detection to Tongue Diagnosis in Traditional Chinese Medicine

2013 ◽  
Vol 756-759 ◽  
pp. 3771-3774
Author(s):  
Yen Sheng Chen ◽  
Yuh Ming Chang ◽  
Jiunn Cherng Lin
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Fundamental Problem ◽  
Intuitionistic Fuzzy Set ◽  
Diagnostic System ◽  
Complex Task ◽  
Edge Information ◽  
Tongue Diagnosis ◽  
Human Tongue ◽  
Canny Algorithm

The tongue diagnosis is an important diagnostic method in Traditional Chinese Medicine (TCM). Human tongue is one of the im­portant organs which contain the information of health status. Image segmentation has always been a fundamental problem and complex task in the field of image processing and computer vision. Its goal is to change the representation of an image into something that is more meaningful and easier to analyze. In other words, it is used to partition a given image into several parts in each of which the intensity is homogeneous. In order to achieve an automatic tongue diagnostic system, an effective segmentation me­thod for detecting the edge of tongue is very important. We mainly compare the Chan Vese Method and Canny algorithm for edge segmentation. The segmentation using Canny algorithm may produce many false edges after cutting; thus, it is not suitable for use. But, for our two steps Chan Vese method can automatically select the best edge information. Therefore, it may be useful in clinical automated tongue diagnosis system. Experiments show the results of these techniques.

scholarly journals Research on healthy catering recommendation space

2020 ◽  
Vol 309 ◽  
pp. 03011
Author(s):  
Yongjie Deng ◽  
Yong Liu ◽  
Dongping Tang
Keyword(s):  
Health Status ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Questionnaire Survey ◽  
Recommendation System ◽  
Constitutional Theory ◽  
Theoretical Knowledge ◽  
Key Factors ◽  
Individual Preferences ◽  
Depth Study

This paper conducts an in-depth study on the catering recommendation space based on different situations. By consulting the literature, visiting relevant experts, condensing the theoretical knowledge of TCM (Traditional Chinese Medicine) constitutional dietotherapy theory, it has extracted some key factors that can be effectively utilized in catering theory. The variable framework of the situational catering recommendation system is preliminarily constructed. Also, it has designed a mixed catering recommendation space that combines TCM Constitutional Theory and user situation, which not only meets individual preferences but also improves the users’ sub-health status. Finally, through the questionnaire survey, the importance of each attribute in the eyes of consumers has been recognized; then this essay provides a guidance for the recommendation ranking method in the design of the catering recommendation system after making the cross-analysis.

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