Wen Jiao1, Xiao-Juan Hu2, Li-Ping Tu1, Chang-le Zhou3, Zhen Qi1, Zhi-Yu Luo1, Ling-Zhi Zeng1, Xu-Xiang Ma1, Ching-Hsuan Pai1, Hong-Yuan Fu1, Yu Wang1, Jue Wang1, Jia-Tuo Xu4. 1. School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai, 201203, China. 2. Shanghai Collaborative Innovation Center of Health Service in Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai, 201203, China. 3. Department of Intelligent Science and Technology, Xiamen University, 422 South Siming Road, Xiamen, 361005, Fujian, China. 4. School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai, 201203, China. xjt@fudan.edu.cn.
Abstract
PURPOSE: Studies have shown the association between tongue color and diseases. To help clinicians make more objective and accurate decisions quickly, we take unsupervised learning to deal with the basic clustering of tongue color in a 2D way. METHODS: A total of 595 typical tongue images were analyzed. The 3D information extracted from the image was transformed into 2D information by principal component analysis (PCA). K-Means was applied for clustering into four diagnostic groups. The results were evaluated by clustering accuracy (CA), Jaccard similarity coefficient (JSC), and adjusted rand index (ARI). RESULTS: The new 2D information totally retained 89.63% original information in the L*a*b* color space. And our methods successfully classified tongue images into four clusters and the CA, ARI, and JSC were 89.04%, 0.721, and 0.890, respectively. CONCLUSIONS: The 2D information of tongue color can be used for clustering and to improve the visualization. K-Means combined with PCA could be used for tongue color classification and diagnosis. Methods in the paper might provide reference for the other research based on image diagnosis technology.
PURPOSE: Studies have shown the association between tongue color and diseases. To help clinicians make more objective and accurate decisions quickly, we take unsupervised learning to deal with the basic clustering of tongue color in a 2D way. METHODS: A total of 595 typical tongue images were analyzed. The 3D information extracted from the image was transformed into 2D information by principal component analysis (PCA). K-Means was applied for clustering into four diagnostic groups. The results were evaluated by clustering accuracy (CA), Jaccard similarity coefficient (JSC), and adjusted rand index (ARI). RESULTS: The new 2D information totally retained 89.63% original information in the L*a*b* color space. And our methods successfully classified tongue images into four clusters and the CA, ARI, and JSC were 89.04%, 0.721, and 0.890, respectively. CONCLUSIONS: The 2D information of tongue color can be used for clustering and to improve the visualization. K-Means combined with PCA could be used for tongue color classification and diagnosis. Methods in the paper might provide reference for the other research based on image diagnosis technology.