Qi Zhen, Tu Li-Ping, Chen Jing-Bo, Hu Xiao-Juan, Xu Jia-Tuo, Zhang Zhi-Feng
Department of Basic Medical College, Shanghai University of Traditional Chinese Medicine, 1200 Cailun Road, Pudong New Area, Shanghai 201203, China.
College of Computer Science and Technology, Jilin University, Jilin 130012, China.
Biomed Res Int. 2016;2016:3510807. doi: 10.1155/2016/3510807. Epub 2016 Dec 6.
. The application of digital image processing techniques and machine learning methods in tongue image classification in Traditional Chinese Medicine (TCM) has been widely studied nowadays. However, it is difficult for the outcomes to generalize because of lack of color reproducibility and image standardization. Our study aims at the exploration of tongue colors classification with a standardized tongue image acquisition process and color correction. . Three traditional Chinese medical experts are chosen to identify the selected tongue pictures taken by the TDA-1 tongue imaging device in TIFF format through ICC profile correction. Then we compare the mean value of of different tongue colors and evaluate the effect of the tongue color classification by machine learning methods. . The values of the five tongue colors are statistically different. Random forest method has a better performance than SVM in classification. SMOTE algorithm can increase classification accuracy by solving the imbalance of the varied color samples. . At the premise of standardized tongue acquisition and color reproduction, preliminary objectification of tongue color classification in Traditional Chinese Medicine (TCM) is feasible.
如今，数字图像处理技术和机器学习方法在中医舌象分类中的应用已得到广泛研究。然而，由于缺乏颜色再现性和图像标准化，研究结果难以推广。我们的研究旨在通过标准化的舌象采集过程和颜色校正来探索舌色分类。选择三位中医专家，通过ICC配置文件校正，识别由TDA-1舌成像设备以TIFF格式拍摄的选定舌象图片。然后我们比较不同舌色的均值，并通过机器学习方法评估舌色分类的效果。五种舌色的均值在统计学上存在差异。在分类中，随机森林方法比支持向量机表现更好。SMOTE算法可以通过解决不同颜色样本的不平衡问题来提高分类准确率。在标准化舌象采集和颜色再现的前提下，中医舌色分类的初步客观化是可行的。
