北京生物医学工程

基于中医舌象特征的痤疮证型分类的初步研究

Preliminary study of acne syndrome classification based on the characteristics of tongue in TCM

作者：张新峰王虹贵明俊胡广芹

单位：北京工业大学电子信息与控制工程学院（北京100124）

分类号：R318.04

出版年·卷·期（页码）：2016·35·5（464-468）

摘要：

目的舌诊是中医诊断痤疮的有效途径，目前中医通过观察舌象来确定痤疮患者的证候类型。由于痤疮患者众多，完全基于人工诊断的效率较低。本文提出一种基于图像处理的痤疮证型识别方法来辅助中医诊断。方法首先，分别提取舌象的颜色、纹理和齿痕特征，然后使用贝叶斯网络建模，找出特征与特征，特征与证型之间的关系，其中将齿痕提取算法进行改进，将计算凸包面积改进为找到每个凸包的关键点，最后使用该算法对舌象进行齿痕数量提取，并与中医诊断结果相比较。结果对比医生诊断结果，基于图像处理的痤疮证型自动分类，分3类的正确率达83.87%，并直观地表示出特征与特征，特征与证型之间的关系。结论使用图像处理的方法进行痤疮证型的识别具有可行性，对计算机辅助痤疮诊断的发展有一定帮助。

Objective Tongue diagnosis is one of the effective ways to diagnose acne. Traditional Chinese medicine (TCM) in treatment of acne is currently mainly determined by doctors to be observed in patients’ tongues. Because the number of patients with acne is numerous, the efficiency completely based on the artificial diagnosis is low. This paper presents a novel method for the diagnosis of acne syndromes with a computer to assist TCM diagnosis. Methods First of all, features of color, texture and teeth marks were extracted from acne tongues. Then we used Bayesian network to simulate the relationship between the features and syndromes, and completed the classification. The original extraction algorithm for teeth marks was improved from calculation of convex hull area to finding each key point of convex hull. Finally we used the algorithm to extract the number of teeth marks and compared with TCM diagnosis. Results The extraction results were consistented with TCM diagnosis. Compared with the TCM diagnosis, the accuracy of acne syndrome classification based on image processing was 83.87%, and intuitively showed the interaction between features and features, features and syndromes. Conclusions The results show the feasibility of image processing for the diagnosis of acne syndromes, which is beneficial to the development of auxiliary diagnosis of acne syndromes in TCM.

参考文献：

［1］蒋依吾, 陈建仲, 张恒鸿, 等. 电脑化中医舌诊系统［J］.中国中西医结合杂志, 2000，(2): 145-147.

Jiang Yiwu, Chen Jiangzhong, Zhang Henghong, et al. A computerized system tongue diagnosis in traditional Chinese medicine［J］. Chinese Journal of Integrated Traditional and Western Medicine, 2000，(2): 145-147.

［2］易霞, 李晟, 秦莉花, 等.贝叶斯分类器在中医证候中的运用［J］.中医研究, 2013, 26(6): 4-6.

Yi Xia,Li Sheng, Qin Lihua, et al. The use of Bayesian classifier in syndromes analysis［J］. Chinese Medicine Research, 2013, 26(6): 4-6.

［3］Wang Hong, Zhang Xinfeng. The preliminary research on feature extraction and classification of acne tongue picture in TCM［C］. Huangshan： International Conference on Internet Multimedia Computing and Service, 2013.

［4］钟少丹, 韦玉科, 谢铮桂.基于凸包的齿痕点快速定位的方法研究［J］.微计算机信息, 2009,26(33):312-314.

Zhong Shaodan, Wei Yuke, Xie Zhenggui. The research on tooth marks fast positioning method based on the convex hull［J］. Microcomputer Information, 2009, 26(33):312-314.

［5］Wang H, Zhang X, Cai Y. Research on teeth marks recognition in tongue image［C］. Medical Biometrics, 2014 International Conference on IEEE, 2014: 80-84.

［6］Sklansky J. Measuring concavity on a rectangular mosaic［J］. IEEE Trans Computers, 1972, 21: 1355-1364.

［7］Pang B, Zhang D, Li N, et al. Computerized tongue diagnosis based on Bayesian networks［J］. IEEE Transactions on Biomedical Engineering, 2004, 51(10):1803-1810.

［8］王双成, 张明, 陈乃激. 基于因果语义定向的贝叶斯网络结构学习［J］.计算机工程与应用, 2007,43(8): 29-31.

Wang Shuangcheng, Zhang Ming, Chen Naiji. Learning Bayesian networks structure based on causal semanitics orienting［J］. Computer Engineering and Applications, 2007, 43(8):29-31.

［9］喻晓锋,秦春影.一种改进的贝叶斯网弧定向算法研究［J］.计算机工程与科学, 2010, 32(3): 82-84.

Yu Xiaofeng, Qin Chunying. Research on a modified Bayesian network arc orienting algorithm［J］. Computer Engineering& Science, 2010, 32(3): 82-84.

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