基于半监督学习的舌色分析方法研究
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摘要
舌诊是中医学中最有临床应用价值的中医诊断方法之一。近年来,随着计算机科学与技术的迅速发展,传统中医舌诊学朝着计算机化方向发展已经成为必然趋势。本文正是试图推广计算机化中医舌诊的发展,着重进行了舌图像颜色训练和分类问题的方法研究。
本文的主要贡献在于:分析了现有舌象颜色分析方法的不足之处;设计了基于像素的舌颜色分类系统结构;提出了一个基于半监督学习的像素分类算法,解决了基于像素的苔色质色分布模型的建立问题;提出了RKNN算法,将一个全局优化问题转化成一个动态局部问题,并将其应用于苔色质色分类中,解决了苔色质色海量像素分类的时间复杂度问题;并计算舌图像的“颜色比例特征向量”,将其应用在对舌图像的总体颜色分类和胰腺炎的自动诊断。
首先,本文系统的总结了现有舌象舌色分析方法的不足之处,并根据这些不足产生的原因,正确地选择了舌象图像中的像素作为分类算法的研究对象。接着提出了一种基于半监督学习的医学生物特征识别的新算法,该算法的性能比监督学习与非监督学习的性能更好。
其次,本文结合前向选择和后向选择的苔色质色分布模型的训练方法,建立了12种苔色质色的分布模型数据集,较好地解决了基于像素的舌颜色分类系统的颜色模型的建立问题,大大提高了训练样本的质量。本文进一步提出RKNN算法,应用其实现对苔色质色的分类,将一个全局优化问题转化成一个动态局部算法,使之适用于舌象图像中大规模像素的分类计算。最后根据舌图像的12维颜色比例特征向量,实现了对舌图像的整体颜色分类。
最后,本文对训练样本和实验结果进行分析,并在中医专家的指导下,对胰腺炎的自动诊断的可行性及其自动诊断效果进行研究,取得了令人满意的成绩。
Tongue Diagnosis is one of the most valuable methods in Traditional Chinese Medicine practice, and its compuerization is inevitable with the rapid development of computer science and technology these years. This dissertation is thus focused on advocating the development of computerized Tongue Diagnosis and researching on the methodology of tongue color training and classification.
Major contributions of this dissertation include: analyzing the weaknesses of the current tongue color analysis methods; designing the algorithm of pixel-based tongue color classification system; proposing a semi-supervised method for tongue pixels classification; converting a global optimization problem to a dynamically local one, therefore significantly improving the processing speed; proposing an eigenvalue of color ratios and applying it to tongue color classification and the automatic diagnosis of pancreatitis.
First of all, the weaknesses of the the current tongue color analysis methods are summarized systemically, based on the reason of which, pixels in the tongue images are selected as the research subjects of the classification. Later, a new medical biometrics algorithm is suggested based on semi-supervised learning with better performance than that of supervised and unsupervised learning methods.
Secondly, by combining forward selection and backward selection, distribution model datasets of 12 tongue colors and pixel-based tongue color model are set up with higher quality training samples. Furthurmore, RKNN algorithm is proposed to classify tongue substance and coating color, converting a global optimization problem into a dynamically local one, suitable for the huge number of the pixels. Finally, a 12-dimension color ratio eigenvalue is applied for the whole tongue color classification.
Last but not least, training samples and experiment result are analyzed. With the guidance and assistance of TCM specialists, the feasibility and the accuracy of the automatic pancreatitis diagnosis are also justified with a satisfying experiment result.
本文的主要贡献在于:分析了现有舌象颜色分析方法的不足之处;设计了基于像素的舌颜色分类系统结构;提出了一个基于半监督学习的像素分类算法,解决了基于像素的苔色质色分布模型的建立问题;提出了RKNN算法,将一个全局优化问题转化成一个动态局部问题,并将其应用于苔色质色分类中,解决了苔色质色海量像素分类的时间复杂度问题;并计算舌图像的“颜色比例特征向量”,将其应用在对舌图像的总体颜色分类和胰腺炎的自动诊断。
首先,本文系统的总结了现有舌象舌色分析方法的不足之处,并根据这些不足产生的原因,正确地选择了舌象图像中的像素作为分类算法的研究对象。接着提出了一种基于半监督学习的医学生物特征识别的新算法,该算法的性能比监督学习与非监督学习的性能更好。
其次,本文结合前向选择和后向选择的苔色质色分布模型的训练方法,建立了12种苔色质色的分布模型数据集,较好地解决了基于像素的舌颜色分类系统的颜色模型的建立问题,大大提高了训练样本的质量。本文进一步提出RKNN算法,应用其实现对苔色质色的分类,将一个全局优化问题转化成一个动态局部算法,使之适用于舌象图像中大规模像素的分类计算。最后根据舌图像的12维颜色比例特征向量,实现了对舌图像的整体颜色分类。
最后,本文对训练样本和实验结果进行分析,并在中医专家的指导下,对胰腺炎的自动诊断的可行性及其自动诊断效果进行研究,取得了令人满意的成绩。
Tongue Diagnosis is one of the most valuable methods in Traditional Chinese Medicine practice, and its compuerization is inevitable with the rapid development of computer science and technology these years. This dissertation is thus focused on advocating the development of computerized Tongue Diagnosis and researching on the methodology of tongue color training and classification.
Major contributions of this dissertation include: analyzing the weaknesses of the current tongue color analysis methods; designing the algorithm of pixel-based tongue color classification system; proposing a semi-supervised method for tongue pixels classification; converting a global optimization problem to a dynamically local one, therefore significantly improving the processing speed; proposing an eigenvalue of color ratios and applying it to tongue color classification and the automatic diagnosis of pancreatitis.
First of all, the weaknesses of the the current tongue color analysis methods are summarized systemically, based on the reason of which, pixels in the tongue images are selected as the research subjects of the classification. Later, a new medical biometrics algorithm is suggested based on semi-supervised learning with better performance than that of supervised and unsupervised learning methods.
Secondly, by combining forward selection and backward selection, distribution model datasets of 12 tongue colors and pixel-based tongue color model are set up with higher quality training samples. Furthurmore, RKNN algorithm is proposed to classify tongue substance and coating color, converting a global optimization problem into a dynamically local one, suitable for the huge number of the pixels. Finally, a 12-dimension color ratio eigenvalue is applied for the whole tongue color classification.
Last but not least, training samples and experiment result are analyzed. With the guidance and assistance of TCM specialists, the feasibility and the accuracy of the automatic pancreatitis diagnosis are also justified with a satisfying experiment result.
引文
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19孙炀,罗瑜,周昌乐,许家佗,张志枫.一种基于分裂-合并方法的中医舌像区域分割算法及其实现.中国图象图形学报, 2003, 8(12): 1395~1399
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21吴芸,周昌乐,张志枫.中医舌诊八纲辨证神经网络知识库构建.计算机应用研究, 2006, 23(6): 188~189
22丁明,张建正.基于L*a*b*彩色模式的舌苔定量描述和分类.仪器仪表学报, 2002, 23(3): 328~330
23丁明.舌象特征参数提取和分类的研究.中国学位论文全文数据库,华东理工大学, 2000
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2 D. Zhang. Automated Biometrics: Technologies and Systems, Kluwer Academic Publisher, 2000
3 D. Zhang. Parallel Computer System Design for Pattern Recognition and Image Processing. HIT Press, 1998
4孙立友.利用计算机图像处理技术进行舌诊客观化研究的探讨.安徽中医学院学报. 1986, (4): 5~7
5 W.L. Weng. The Development of Modern Tongue Diagnosis. The Illustrations of Clinical Tongue Diagnosis and Disease Treatments. Shed-Yuan Publishing, Peking, 1997: 26~49
6 Zhao Zhongxu, Wang Aimin, Shen Lansun, et al. An Automatic Tongue Analyzer of Chinese Medicine Based on Color Image Processing. 4th International Conference on Electronic Measurement & Instruments Conference Proceedings, Harbin, China, 1999: 830~834
7 Wang Aimin, Shen Lansun, Zhao Zhongxu. Fuzzy Automatic Detecting the Thickness of Tongue-covering from a Tongue Image. 4th International Conference on Electronic Measurement & Instruments Conference Proceedings, Harbin, China, 1999: 863~867
8卫保国,沈兰荪,王艳清,王永刚,王爱民,赵忠旭.数字化中医舌象分析仪.中国医疗器械杂志, 2002, 26(3): 164~167
9沈兰荪,王爱民,卫保国,王永刚,赵忠旭.图像分析技术在舌诊客观化中的应用.电子学报, 2001, 29(12A): 1762~1765
10卫保国,沈兰荪,蔡轶珩.舌体歪斜的自动分析.计算机工程与应用, 2003, (25): 22~25
11沈兰荪,蔡轶珩,卫保国,张新峰,王艳清.中医舌象分析技术的研究.世界科学技术-中药现代化, 2003, 5(1): 15~19
12王爱民,赵忠旭,沈兰荪.中医舌象自动分析中舌色、苔色分类方法的研究.北京生物医学工程, 2000, 19(03): 136~141
13宋恺,蔡轶珩,沈兰荪.中医舌图像分析管理信息系统的构建.中国生物医学工程学会中医药工程学会2005年学术年会论文集, 2005: 21~28
14英杰,李重锡,李梢,季梁,刘德麟,马维娅.脑血管病患者舌象特征的提取与分析方法.北京中医药大学学报, 2005, 28(4): 62~66
15刘关松,徐建国,高敦岳.基于神经网络集成的舌苔分类方法.计算机工程, 2003, 29(14): 100~102
16连奕劭.计算机辅助的中医舌诊系统.中国学位论文全文数据库,浙江大学, 2005
17陈海燕,卜佳俊,龚一萍,连奕劭.一种基于多色彩通道动态阈值的舌苔舌质分离算法.北京生物医学工程, 2006, 25(5): 466~469
18诸薇娜,周昌乐,徐丹,许家佗.基于颜色纹理的图像多特征检索技术在中医舌诊中的应用研究.中国图象图形学报, 2005, 10(8): 992~998
19孙炀,罗瑜,周昌乐,许家佗,张志枫.一种基于分裂-合并方法的中医舌像区域分割算法及其实现.中国图象图形学报, 2003, 8(12): 1395~1399
20周昌乐,张志枫.智能中医诊断信息处理技术研究进展与展望.中西医结合学报, 2006, 4(6): 560~566
21吴芸,周昌乐,张志枫.中医舌诊八纲辨证神经网络知识库构建.计算机应用研究, 2006, 23(6): 188~189
22丁明,张建正.基于L*a*b*彩色模式的舌苔定量描述和分类.仪器仪表学报, 2002, 23(3): 328~330
23丁明.舌象特征参数提取和分类的研究.中国学位论文全文数据库,华东理工大学, 2000
24王慧燕.中医多维信息集成式诊断智能系统应用基础研究.中国学位论文全文数据库,浙江大学, 2005
25陈松鹤,梁嵘,王召平,张永涛,樊艳.健康体检人群中齿痕舌的研究.辽宁中医杂志, 2007, 34(1): 20~21
26樊艳,梁嵘,王召平,王立翔.关于数字舌图色彩校正方法的探讨.医药世界, 2006, (11): 34~36
27梁嵘,张永涛,王召平,陈松鹤,李方玲,樊艳.关于数字舌图的群体特征研究.中医药学刊, 2006, 24(5): 779~781
28陈松鹤,梁嵘.纹理分析技术应用于舌象研究的问题与对策.世界科学技术-中医药现代化, 2006, 8(5): 22~25
29张永涛,梁嵘,王召平,李方玲,樊艳. 884例体检人群舌色数字图像应用不同颜色模型的比较.中国中医基础医学杂志, 2005, 11(3): 207~209
30田振兴.舌象信息的采集处理及模式识别.中国学位论文全文数据库.南京理工大学, 2003
31 J. H. Jang, et al. Development of Digital Tongue Inspection System with Image Analysis. Proceedings of the Second Joint EMBS Conference, 2002, 2: 1033~1034
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