Comparing Chan Vese Method and Canny Algorithm for Edge Detection to Tongue Diagnosis in Traditional Chinese Medicine

2013 ◽  
Vol 13 (22) ◽  
pp. 5468-5472
Author(s):  
Yen-Sheng Chen ◽  
Jiunn-Cherng Lin ◽  
Yuh-Ming Chang
Keyword(s):  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Edge Detection ◽  
Tongue Diagnosis ◽  
Canny Algorithm

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.

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.

scholarly journals ROC-Boosting: A Feature Selection Method for Health Identification Using Tongue Image

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Yan Cui ◽  
Shizhong Liao ◽  
Hongwu Wang
Keyword(s):  
Feature Selection ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Health Information ◽  
Scientific Evidence ◽  
Feature Selection Method ◽  
Selection Method ◽  
Tongue Diagnosis ◽  
Health Group ◽  
Boosting Method

Objective. To select significant Haar-like features extracted from tongue images for health identification.Materials and Methods. 1,322 tongue cases were included in this study. Health information and tongue images of each case were collected. Cases were classified into the following groups: group containing 148 cases diagnosed as health; group containing 332 cases diagnosed as ill based on health information, even though tongue image is normal; and group containing 842 cases diagnosed as ill. Haar-like features were extracted from tongue images. Then, we proposed a new boosting method in the ROC space for selecting significant features from the features extracted from these images.Results. A total of 27 features were obtained from groups A, B, and C. Seven features were selected from groups A and B, while 25 features were selected from groups A and C.Conclusions. The selected features in this study were mainly obtained from the root, top, and side areas of the tongue. This is consistent with the tongue partitions employed in traditional Chinese medicine. These results provide scientific evidence to TCM tongue diagnosis for health identification.

scholarly journals Breast Cancer Index: A Perspective on Tongue Diagnosis in Traditional Chinese Medicine

2013 ◽  
Vol 3 (3) ◽  
pp. 194-203 ◽  
Author(s):  
Lun-Chien Lo ◽  
Tsung-Lin Cheng ◽  
John Y. Chiang ◽  
Natsagdorj Damdinsuren
Keyword(s):  
Breast Cancer ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Tongue Diagnosis ◽  
Breast Cancer Index
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