舌数字图像颜色计算机分析与分类
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摘要
[目的]
舌诊是中医诊断学中一种具有特色的常规诊法,舌象颜色是中医病证诊断的重要指标之一,诊断价值非常重要。然而,传统中医舌诊,包括舌质颜色、舌苔颜色的观察与诊断,都有着标准化、定量化、客观化的问题,具体表现为：①诊断人与被诊断人的状态随时变化,无法保持恒定,无法标准化,即使是具有丰富舌诊经验的高年资医生,在不同环境、不同时间、甚至不同心理状态下,其诊察结果往往也有一定差异。②传统舌诊仅作出如“淡红舌”、“青紫舌”、“白苔”、“黄苔”、“舌尖红刺”之类的定性判断,无法给出定量化的分析结果。③主观因素影响较大,不同医生的舌象颜色、苔色诊断往往存在一定差异,虽然具有丰富舌诊经验的医生其诊断结果基本趋于一致,但普通医生的判断往往带有较大的主观色彩,缺少客观化依据。
总之,传统舌诊主要是通过医生的视觉观察、语言文字描述经验辨析得出诊断结论,其结果受到医生的知识水平、思维能力和诊断技能的限制,因而严重制约舌象颜色临床应用及进一步发展,舌数字图像颜色计算机分析,特别是舌象颜色的计算机客观化、标准化、定量化研究已经成为中医舌诊现代化的主要研究内容。
为了解决上述问题,本论文对中医舌诊现代化过程中舌象颜色计算机特征分析等内容进行了初步的研究,主要包括：
[方法]
首先,本论文介绍了舌象颜色计算机特征分析的预处理流程中舌象颜色校正专用色卡的定制方法。对于舌数字图像采集设备,舌象颜色校正的研究者们大都将目光聚焦在计算机图像分析与分类的其他环节,却没有对舌象颜色校正中用到的标准色卡进行深入研究,在实际应用中严重影响了舌象颜色校正的效果；更没有研究者采用合理的方法来设计标准色卡,造成舌象颜色校正的方法应用并不普遍。
然后,由于舌数字图像采集设备的不断完善,医学图像数据已经从数量、内容和多元化方向上快速发展起来。而正是这些发展,导致了人们对高效率医学图像数据的查找和管理的需求增长。现阶段纯文本的查找方法无法充分的描述丰富的视觉特征和图像特征,因此给医学图像数据、特别是舌数字图像的查找造成了巨大的障碍。本文利用基于相对熵的门限值分割算法,获取“模板像素”样本,然后利用改进的K最近邻算法进行像素微观化分类,并从已区分为5个不同区域的舌体上提取20维特征矢量,最终提出基于累积直方图的一种距离度量进行图像查找和匹配。
其三,本文提出了一个半监督学习的模型,通过舌象颜色将舌数字图像上的所有像素区分为3类：冷、暖或过渡色。这个模型首先利用最大期望聚类算法,将所有的像素区分为很多个聚类,在这次聚类过程中,聚类的数量较大(共150个),而每个聚类尺度较小。然后人工标定将分别赋予这些聚类3种舌象颜色冷暖属性的类别标签：冷色、暖色、过渡色。最后使用一个查找表的数据结构将所有像素区分为舌象颜色冷暖属性的三个类别,并以此为依据,将舌数字图像样本区分为“寒证”或“热证”的总体分类。
其四,本文提出了一个半监督学习方法来进行舌象颜色总体分类。本文利用“聚类然后标定”的层次化聚类方法,对训练集合中所有舌数字图像上的所有像素进行合并,并对完成合并过程后的舌象颜色域集合进行聚类,构建若干尺度较小且数量众多的像素子集及其对应图像,利用每个舌数字图像中各像素子集所占比例构建特征矢量,进行舌象颜色总体分类。
最后,本文提出了一个将定制色卡的在线和离线采集方式结合起来提高图像校正质量与精度的融合系统。试图在定制色卡的基础上,结合前述层次化聚类的思想,得到合理的定制色卡标准值,并通过多次迭代舌象颜色校正的方式,进一步提高颜色校正的图像质量与精度。
[结果]
(1)本文利用均匀实验设计方法,将定制色卡中每一个色块当作一次“实验”,色块的颜色取值则作为实验参数的水平数,既合理的增加了校正色卡的数量,又可能使得校正色卡的定制变得合理而简单易行。
(2)本文提出了一个快速而有效的目标匹配算法,用来在大量的舌数字图像数据中查找相似的舌苔图像。实验结果证明这种非参数的框架不仅给舌诊构建了一个客观的基础,而且有希望在临床中得到应用。
(3)本章提出了一个SSL方法将一个舌数字图像中的所有像素区分为舌象颜色冷暖属性的3个类别。该方法的主要优点是其快速而高效,并可以用于大规模数据中,它减少了计算机舌诊中主观性和不量化,并建立起舌数字图像中每个像素与其舌象颜色冷暖属性类别标签的联系。与现有的方法相比,本章解决了标定数据较少而未标定数据较多之间的矛盾,实验结果合理的证明了方法的有效性。
(4)本文提出了一个基于量化矢量的方法来进行舌数字图像颜色计算机分析,并构建了像素与舌象颜色类别之间的关系模型。该方法的有效性在418个样本上得到测试。实验结果证明,本方法不仅有利于舌数字图像颜色计算机分析,也优于传统的方法,从而该方法有了应用于临床的可能性。
(5)这样的框架也有利于将其他主观的舌象症状描述量化成相对客观的舌象特征,使得舌象颜色统计结果更符合中医舌诊临床和研究的需要,最大程度的降低了颜色样本筛选过程中的主观因素和计算误差
[结论]
本论文在舌象颜色特征提取和分析方面进行了有益的、探索性的尝试,为中医舌诊自动化诊断提供了一种有效的解决途径。
Objective:
Traditional Chinese Medicine (TCM) includes a range of traditional medical practices originated from China. Examination of the condition of the tongue is one of the most valuable and widely used diagnostic methods in TCM diagnostics. In tongue diagnostics, changes in tongue color are sensitive indicators of internal pathological symptom and indispensable guideline for overall health state. However, the further development of traditional tongue diagnosis is limited by its dependence on individual visual sensation and experience. There are three problems in the field of tongue color analysis. The first one is the standardization of observation status. The second one is that TCM only offer the results of qualitative analysis, without any results of quantitative analysis. The third one is the subjectivity in the diagnostic results. In a word, this will undoubtedly limit the application of tongue color analysis in clinical practice. In recent years, many researchers are dedicated to the combination of modern pattern recognition and image processing technologies, trying to find solutions to the non-quantitative issue of traditional tongue diagnosis.
In this report of computerized tongue diagnosis, we investigate tongue color analysis and diagnosis classification, including:
Methods:
(1) The color checker customization is introduced. This is a part of preprocess of tongue color analysis. We use the Uniform Experimental Design to select the best group of tongue color checker.
(2) Along with the rapid growth of medical data, image retrieval, a kind of technology for browsing, searching and retrieving similar images of the given image, has become increasingly important from a large database of digital images. Tongue coating is the most important characteristic to reveal the pathological changes of the tongues for identifying diseases. In this paper, an efficient and effective technique is proposed to retrieve coating images. We obtain the pixel template value of pixels by applying thresholding segmentation based relative entropy. Then we use a Reduced K Nearest Neighbor algorithm to extract20-dimension feature vector based on a prior layout distribution. Finally, a distance based the cumulative ratio is proposed for tongue coating image matching.
(3) Examination of the tongue condition is a standard diagnostic method in Traditional Chinese Medicine (TCM) and takes account of a wide variety of features including shape, texture, and color. The terms "warm","neutral", and "cool" are used to refer to a kind of chromatics characteristic of the tongue color and are associated with various health states. In this paper, we propose a semi-supervised scheme for tongue color analysis on "warm or cool". In its training part, the proposed scheme firstly makes use of a classical clustering algorithm, Expectation Maximization, to divide all pixels in tongue gamut into many clusters. Secondly, we construct two auxiliary images for each cluster and manual labeling endows each cluster with a category label of "warm or cool". Thirdly, each trained (or labeled) category on "warm or cool" is set up by sum some clusters approximately. Finally, in the testing part, we use a lookup table to divide all pixels in an input image into three distinct categories of "warm or cool"
(4) Tongue diagnosis is a distinctive and essential diagnostic method. The color category of the tongue can be utilized to discover pathological changes on the tongues for identifying diseases. In this paper, a novel scheme is established which classify tongue images into various categories, including coating and substance categories. Firstly, we proposed a two level hierarch clustering method for quantizing all pixels into numerous vectors of feature value. Each vector can code a very small sub-class in RGB color space. Secondly, we utilized the vectors'distribution of these sub-classes to represent approximate chromatic information of tongue images. Then, a Bayesian Network is employed to model the relationship between these quantized vectors and tongue color categories.
(5) Finally, we proposed a fusion system, which combine the online and offline methods of customized color checker to improve the accuracy and quality of the image color correction. Combining the idea of hierarchy clustering which we mentioned before, the accurate values of each color part in the color checker can calculated based on the customized color checker.
Results:
(1) In this way, the quantity of color checker is small and the design process of color checker customization is very easy.
(2) The experimental results indicate that the proposed scheme eliminates the imprecision and uncertainty associated with medical tongue coating analysis.
(3) In experiments conducted on a total of392tongue samples, our system achieved an accuracy of91.1%.
(4) The effectiveness of this scheme is tested on a group of418tongue images, and the classification results are reported.
(5) And the accuracy and quality of the image color correction can be improved by iterative algorithm. The subjective factors and calculation errors are limited at utmost by this fusion system.
Conclusions:
In this report, we carry out some investigations on tongue color analysis and classification, which will be helpful for the computerized tongue diagnosis.
舌诊是中医诊断学中一种具有特色的常规诊法,舌象颜色是中医病证诊断的重要指标之一,诊断价值非常重要。然而,传统中医舌诊,包括舌质颜色、舌苔颜色的观察与诊断,都有着标准化、定量化、客观化的问题,具体表现为：①诊断人与被诊断人的状态随时变化,无法保持恒定,无法标准化,即使是具有丰富舌诊经验的高年资医生,在不同环境、不同时间、甚至不同心理状态下,其诊察结果往往也有一定差异。②传统舌诊仅作出如“淡红舌”、“青紫舌”、“白苔”、“黄苔”、“舌尖红刺”之类的定性判断,无法给出定量化的分析结果。③主观因素影响较大,不同医生的舌象颜色、苔色诊断往往存在一定差异,虽然具有丰富舌诊经验的医生其诊断结果基本趋于一致,但普通医生的判断往往带有较大的主观色彩,缺少客观化依据。
总之,传统舌诊主要是通过医生的视觉观察、语言文字描述经验辨析得出诊断结论,其结果受到医生的知识水平、思维能力和诊断技能的限制,因而严重制约舌象颜色临床应用及进一步发展,舌数字图像颜色计算机分析,特别是舌象颜色的计算机客观化、标准化、定量化研究已经成为中医舌诊现代化的主要研究内容。
为了解决上述问题,本论文对中医舌诊现代化过程中舌象颜色计算机特征分析等内容进行了初步的研究,主要包括：
[方法]
首先,本论文介绍了舌象颜色计算机特征分析的预处理流程中舌象颜色校正专用色卡的定制方法。对于舌数字图像采集设备,舌象颜色校正的研究者们大都将目光聚焦在计算机图像分析与分类的其他环节,却没有对舌象颜色校正中用到的标准色卡进行深入研究,在实际应用中严重影响了舌象颜色校正的效果；更没有研究者采用合理的方法来设计标准色卡,造成舌象颜色校正的方法应用并不普遍。
然后,由于舌数字图像采集设备的不断完善,医学图像数据已经从数量、内容和多元化方向上快速发展起来。而正是这些发展,导致了人们对高效率医学图像数据的查找和管理的需求增长。现阶段纯文本的查找方法无法充分的描述丰富的视觉特征和图像特征,因此给医学图像数据、特别是舌数字图像的查找造成了巨大的障碍。本文利用基于相对熵的门限值分割算法,获取“模板像素”样本,然后利用改进的K最近邻算法进行像素微观化分类,并从已区分为5个不同区域的舌体上提取20维特征矢量,最终提出基于累积直方图的一种距离度量进行图像查找和匹配。
其三,本文提出了一个半监督学习的模型,通过舌象颜色将舌数字图像上的所有像素区分为3类：冷、暖或过渡色。这个模型首先利用最大期望聚类算法,将所有的像素区分为很多个聚类,在这次聚类过程中,聚类的数量较大(共150个),而每个聚类尺度较小。然后人工标定将分别赋予这些聚类3种舌象颜色冷暖属性的类别标签：冷色、暖色、过渡色。最后使用一个查找表的数据结构将所有像素区分为舌象颜色冷暖属性的三个类别,并以此为依据,将舌数字图像样本区分为“寒证”或“热证”的总体分类。
其四,本文提出了一个半监督学习方法来进行舌象颜色总体分类。本文利用“聚类然后标定”的层次化聚类方法,对训练集合中所有舌数字图像上的所有像素进行合并,并对完成合并过程后的舌象颜色域集合进行聚类,构建若干尺度较小且数量众多的像素子集及其对应图像,利用每个舌数字图像中各像素子集所占比例构建特征矢量,进行舌象颜色总体分类。
最后,本文提出了一个将定制色卡的在线和离线采集方式结合起来提高图像校正质量与精度的融合系统。试图在定制色卡的基础上,结合前述层次化聚类的思想,得到合理的定制色卡标准值,并通过多次迭代舌象颜色校正的方式,进一步提高颜色校正的图像质量与精度。
[结果]
(1)本文利用均匀实验设计方法,将定制色卡中每一个色块当作一次“实验”,色块的颜色取值则作为实验参数的水平数,既合理的增加了校正色卡的数量,又可能使得校正色卡的定制变得合理而简单易行。
(2)本文提出了一个快速而有效的目标匹配算法,用来在大量的舌数字图像数据中查找相似的舌苔图像。实验结果证明这种非参数的框架不仅给舌诊构建了一个客观的基础,而且有希望在临床中得到应用。
(3)本章提出了一个SSL方法将一个舌数字图像中的所有像素区分为舌象颜色冷暖属性的3个类别。该方法的主要优点是其快速而高效,并可以用于大规模数据中,它减少了计算机舌诊中主观性和不量化,并建立起舌数字图像中每个像素与其舌象颜色冷暖属性类别标签的联系。与现有的方法相比,本章解决了标定数据较少而未标定数据较多之间的矛盾,实验结果合理的证明了方法的有效性。
(4)本文提出了一个基于量化矢量的方法来进行舌数字图像颜色计算机分析,并构建了像素与舌象颜色类别之间的关系模型。该方法的有效性在418个样本上得到测试。实验结果证明,本方法不仅有利于舌数字图像颜色计算机分析,也优于传统的方法,从而该方法有了应用于临床的可能性。
(5)这样的框架也有利于将其他主观的舌象症状描述量化成相对客观的舌象特征,使得舌象颜色统计结果更符合中医舌诊临床和研究的需要,最大程度的降低了颜色样本筛选过程中的主观因素和计算误差
[结论]
本论文在舌象颜色特征提取和分析方面进行了有益的、探索性的尝试,为中医舌诊自动化诊断提供了一种有效的解决途径。
Objective:
Traditional Chinese Medicine (TCM) includes a range of traditional medical practices originated from China. Examination of the condition of the tongue is one of the most valuable and widely used diagnostic methods in TCM diagnostics. In tongue diagnostics, changes in tongue color are sensitive indicators of internal pathological symptom and indispensable guideline for overall health state. However, the further development of traditional tongue diagnosis is limited by its dependence on individual visual sensation and experience. There are three problems in the field of tongue color analysis. The first one is the standardization of observation status. The second one is that TCM only offer the results of qualitative analysis, without any results of quantitative analysis. The third one is the subjectivity in the diagnostic results. In a word, this will undoubtedly limit the application of tongue color analysis in clinical practice. In recent years, many researchers are dedicated to the combination of modern pattern recognition and image processing technologies, trying to find solutions to the non-quantitative issue of traditional tongue diagnosis.
In this report of computerized tongue diagnosis, we investigate tongue color analysis and diagnosis classification, including:
Methods:
(1) The color checker customization is introduced. This is a part of preprocess of tongue color analysis. We use the Uniform Experimental Design to select the best group of tongue color checker.
(2) Along with the rapid growth of medical data, image retrieval, a kind of technology for browsing, searching and retrieving similar images of the given image, has become increasingly important from a large database of digital images. Tongue coating is the most important characteristic to reveal the pathological changes of the tongues for identifying diseases. In this paper, an efficient and effective technique is proposed to retrieve coating images. We obtain the pixel template value of pixels by applying thresholding segmentation based relative entropy. Then we use a Reduced K Nearest Neighbor algorithm to extract20-dimension feature vector based on a prior layout distribution. Finally, a distance based the cumulative ratio is proposed for tongue coating image matching.
(3) Examination of the tongue condition is a standard diagnostic method in Traditional Chinese Medicine (TCM) and takes account of a wide variety of features including shape, texture, and color. The terms "warm","neutral", and "cool" are used to refer to a kind of chromatics characteristic of the tongue color and are associated with various health states. In this paper, we propose a semi-supervised scheme for tongue color analysis on "warm or cool". In its training part, the proposed scheme firstly makes use of a classical clustering algorithm, Expectation Maximization, to divide all pixels in tongue gamut into many clusters. Secondly, we construct two auxiliary images for each cluster and manual labeling endows each cluster with a category label of "warm or cool". Thirdly, each trained (or labeled) category on "warm or cool" is set up by sum some clusters approximately. Finally, in the testing part, we use a lookup table to divide all pixels in an input image into three distinct categories of "warm or cool"
(4) Tongue diagnosis is a distinctive and essential diagnostic method. The color category of the tongue can be utilized to discover pathological changes on the tongues for identifying diseases. In this paper, a novel scheme is established which classify tongue images into various categories, including coating and substance categories. Firstly, we proposed a two level hierarch clustering method for quantizing all pixels into numerous vectors of feature value. Each vector can code a very small sub-class in RGB color space. Secondly, we utilized the vectors'distribution of these sub-classes to represent approximate chromatic information of tongue images. Then, a Bayesian Network is employed to model the relationship between these quantized vectors and tongue color categories.
(5) Finally, we proposed a fusion system, which combine the online and offline methods of customized color checker to improve the accuracy and quality of the image color correction. Combining the idea of hierarchy clustering which we mentioned before, the accurate values of each color part in the color checker can calculated based on the customized color checker.
Results:
(1) In this way, the quantity of color checker is small and the design process of color checker customization is very easy.
(2) The experimental results indicate that the proposed scheme eliminates the imprecision and uncertainty associated with medical tongue coating analysis.
(3) In experiments conducted on a total of392tongue samples, our system achieved an accuracy of91.1%.
(4) The effectiveness of this scheme is tested on a group of418tongue images, and the classification results are reported.
(5) And the accuracy and quality of the image color correction can be improved by iterative algorithm. The subjective factors and calculation errors are limited at utmost by this fusion system.
Conclusions:
In this report, we carry out some investigations on tongue color analysis and classification, which will be helpful for the computerized tongue diagnosis.
引文
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14 Yi Feng, Zhaohui Wu, Xuezhong Zhou, Zhongmei Zhou, Weiyu Fan, Knowledge discovery in traditional Chinese medicine:State of the art and perspectives, Artificial Intelligence in Medicine (2006) 38,219—236
15 E. Ernst, S, Mills, Complementary Medicine:A Definition, Br. J. Cen. Pract,1995,45:506
16 E. Ernst, The Role of Complementary and Alternative Medicine, BMJ, 2000,321:1133
17 David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang, Advanced Biometric Recognition Technologies:Discriminant Criterion and Fusion Applications, In Press
18 D.Hoyer, K.Schmidt et.al, Nonlinear analysis of heart rate and respiratory dynamics, IEEE engineering in medicine and biology, Jan/Feb,1997, 31-39
19 Yoshio Maniwa, Tadashi Iokibe, Masaya Koyama, The Application of Pulse Wave Chaos in Clinical Medicine,17th Fuzzy System Symposium, 787-790
20 J. Kittler, M. Hatef, R. P. Duin, J. G. Matas, On combining classifiers, IEEE Transactions on PAMI 20 (3) (1998):226-239
21 E. Bigun, J. Bigun, B. Duc, S. Fischer, Expert conciliation for multi- modal person authentication systems using Bayesian Statistics, in:First International Conference on AVBPA, Crans-Montana, Switzerland, 1997,:291-300
22 T. K. Ho, J. J. Hull, S. N. Srihari, Decision combination in multiple classifier systems, IEEE Transactions on PAMI 16 (1) (1994):66-75
23 Naimin. Li, David. Zhang and Kuanquan. Wang, Tongue Diagnostics, Shed-Yuan Publishing,2006:78,:92-103
24中医药学名词审定委员会,中医药学名词,科学出版社,2005：12-13
25庄泽澄,中医诊断学,科学出版社,1999：27-37
26 Brent. Berlin and Paul Kay, Basic Color Terms:Their Universality and Evolution, Berkeley, CA:Univ. of California Press,1969
27 R.M. Boynton, Human Color Vision, Optical Society of America,1979
28 R.M. Evans, An Introduction to color, John Wiley & Sons,1948
29 Commission Internationale de l'Eclairage, No.15.2, In Colorimetry,2nd ed., volume 1, page 11, Vienna, Austria,1976
30 M.J.Vrhel, Color Imaging:Current Trends and Beyond, IEEE International Conference on Image Processing, Vancouver, BC, Canada,2000:513-516
31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,(14)
32张新峰,沈兰荪,卫保国,蔡轶珩,多类支撑向量机在中医舌质、舌苔分类和识别的应用研究,电路与系统学报,2004,(05)
33张新峰,沈兰荪,现代信息融合技术应用于中医舌象分析的初步研究,测控技术,2005,(05)
34 D. Zhang, B. Pang, N.M. Li, K.Q. Wang, H.Z. Zhang, Computerized Diagnosis from Tongue Appearance Using Quantitative Feature Classification, The American Journal of Chinese Medicine,2005,33(6): 859-866
35 Wenshu Li, Jianglong Xu,Changle Zhou, Zhifeng Zhang, A Novel Method of Tongue. Image Recognition, Proceedings of the Fifth International Conference on Computer and Information Technology, 2005,:586-590
36 B. Pang, D. Zhang, and K. Wang, Tongue image analysis for appendicitis diagnosis, Information Science, vol.175,:160-176,2005
37张新峰,沈兰荪,多特征融合技术应用于中医舌象分析的初步研究,电子学报,2006,(04)
38 H. Wang and X. Zong, A new computerized method for tongue classification, in Proc. IEEE Int. Conf. on Intelligent Systems Design and Applications,2006, vol.2,:508-511
39张衡翔,李斌,姚鹏,庄镇泉,岳小强,中医舌象自动分类方法研究,北京生物医学工程,2006,(01)
40 Y. Wang, J. Yang, Y. Zhou, and Y. Wang, Region partition and feature matching based color recognition of tongue image, Pattern Recognition Letter, vol.28 (1)::11-19, Jan.2007
41 Zhong Gao, Laiman Po, Wu Jiang, Xin Zhao, Hao Dong, A Novel Computerized Method Based on Support Vector Machine for Tongue Diagnosis, Third International IEEE Conference on Signal-Image Technologies and Internet-Based System,2007. SITIS'07.:849-854
42 Siu Cheung Hui, Yulan He, Doan Thi Cam Thach, Machine learning for tongue diagnosis,6th International Conference on Information, Communications & Signal Processing,2007,:1-5
43张萌,胡显伟,王元斌,张斌,AdaBoost算法在中医舌诊图像分区识别中的研究,小型微型计算机系统,2008,(06)
44 Lijun Jiang, Wang Xu, Jianfeng Chen, Digital imaging system for physiological analysis by tongue color inspection,3rd IEEE Conference on Industrial Electronics and Applications,2008, ICIEA,:1833-1836
45 Kim, K.H., Do, J.-H., Ryu, H., Kim, J.-Y., Tongue diagnosis method for extraction of effective region and classification of tongue coating, First Workshops on Image Processing Theory, Tools and Applications,2008, IPTA 2008,:1-7
46胡申宁,李文书,施国生,何芳芳,基于PCA-AdaBoost的舌象颜色分类研究,广西师范大学学报(自然科学版),2009,(03)
47 Yuke Wei, Jiangping Li, Qun Chen, Mei Liu, A new pattern recognition algorithm and its application about tongue fur image classification of Traditional Chinese Medicine,3rd International Conference on Biomedical Engineering and Informatics (BMEI),2010,:37-40
48 Weitong Huang, Zhaoqian Yan, Jiatuo Xu, Li Zhang, Analysis of the tongue fur and tongue features by naive Bayesian classifier, International Conference on Computer Application and System Modeling (ICCASM), 2010,:304-308
49 Yue Jiao, Xinfeng Zhang, Li Zhuo, Mingrui Chen, Kai Wang, Tongue image classification based on Universum SVM,3rd International Conference on Biomedical Engineering and Informatics (BMEI),2010, 657-660
50王爱民,沈兰荪,赵忠旭,监督FCM聚类算法及其在中医舌像自动分类中的应用,模式识别与人工智能,1999,(04)
51 Li, C.H, Yuen, P.C, Regularized color clustering in medical image database, IEEE Trans. Medical Imaging,2000,19 (11):1150-1155
52 Du Jian-qiang, Lu Yan-sheng, Zhang Kang, Zhu Ming-feng, Ding Cheng-hua, A Novel Approach of Tongue Body and Tongue Coating Separation Based on FCM, The 2nd International Conference on Bioinformatics and Biomedical Engineering IEEE,:2499-2503
53郭宙,杨学智,司银楚,朱庆文,牛欣,沙洪,基于K-均值聚类的常用色彩空间舌质舌苔分割研究,北京中医药大学学报,2009,(12)
54郭宙,杨学智,司银楚,朱庆文,牛欣,沙洪,一种基于K-均值聚类的CIELAB和HSI色彩空间舌质舌苔分割算法,中华中医药杂志,2010,(05)
55 J. H. Jang, J. E. Kim, K. M. Park, S. O. Park, Y. S. Chang, and B. Y. Kim, Development of digital tongue inspection system with image analysis, in Proc. IEEE EMBS/BMES,2002,:1033-1034
56余兴龙,竺子民,金国藩,谭耀麟,中医舌诊自动识别系统,仪器仪表学报,1994,(01)
57余兴龙,谭耀麟,竺子民,索忠堃,金国藩,翁维良,许秀森,葛文津,中医舌诊自动识别方法的研究,中国生物医学工程学报,1994,(04)
58樊艳,梁嵘,王召平,王立翔,数字舌图的色彩校正方法研究,世界科学技术-中医药现代化,2005,(04)
59陈松鹤,梁嵘,王盛花,王召平,黄庆梅,数字舌图的舌象颜色数据采集方法研究,世界科学技术-中医药现代化,2007,(02)
60徐晓昭,蔡轶珩,刘长江,贾克斌,沈兰荪,基于图像分析的偏色检测及颜色校正方法,测控技术,2008年05期
61徐晓昭,沈兰荪,刘长江,颜色校正方法及其在图像处理中的应用,计算机应用研究,2008年08期
62许家佗,张志枫,严竹娟,屠立平,陆璐明,史美育,朱凤兰,自然光条件下基于色差校正方法的舌象颜色分析,中西医结合学报2009,(05)
63许家佗,屠立平,张志枫,邱锡鹏,郭跃飞,危辉,一种基于LAB色空间拓扑剖分影射的颜色恒常性模型,电子学报,2009年09期
64晏峻峰,季梁,施诚,基于图像分析技术的开放式舌象研究平台的构建,医学信息,2004,(01)
65张晓丹,王忆勤,基于计算机技术的舌诊客观化检测仪器的临床应用概况,辽宁中医杂志,2009,(01)
66周纪芗,实用相关分析分析方法,上海科学技术出版社,(1990)
67唐启义、冯明光,实用统计分析及其DPS数据处理系统,科学出版社,北京,(2002)
68方开泰、马长兴,正交与均匀实验设计,科学出版社,北京,(2001)
69 Hickernell, F.J., Goodness-of-fit statistics, discrepancies and robust designs, Statist, Probab. Lett.,44,73-78, (1999)
70张宏志,舌数字图像获取与分析技术研究,哈尔滨工业大学博士论文,2007：12-13
71 Steve Gunn, Support Vector Machines for Classification and Regression, ISIS Technical Report,1998
72张学工,模式识别(第三版),北京：清华大学出版社,2000
73李晶皎,赵丽红,王爱侠,模式识别,北京：电子工业出版社,2010
74 Chen Li,Yap Mon-Tue,A fuzzy K-nearest-neighbour algorithm to blind image deconvolution, IEEE International Conference on Systems, Man and Cybernetics,2003
75 Xiaojin Zhu, Semi-Supervised Learning with Graphs, Ph.D dissertation, Dept. Electron. Eng., Carnegie Mellon University,2005
76 Y.-F. Li and Z.-H. Zhou, Improving semi-supervised support vector machines through unlabeled instances selection, In:Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI'11), San Francisco, CA,2011,:386-391
77 Y.-F. Li, J. Kwok, and Z.-H. Zhou, Cost-sensitive semi-supervised support vector machine, In:Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI'10), Atlanta, GA,2010,:500-505
78 Z.-H. Zhou and M. Li, Semi-supervised learning by disagreement, Knowledge and Information Systems,2010,24(3):415-439
79 J.-M. Xu, G. Fumera, F. Roli, and Z.-H. Zhou. Training Spam Assassin with active semi-supervised learning, In:Proceedings of the 6th Conference on Email and Anti-Spam (CEAS'09), Mountain View, CA, 2009
80 Y.-F. Li, J. T. Kwok, and Z.-H. Zhou, Semi-supervised learning using label mean, In:Proceedings of the 26th International Conference on Machine Learning (ICML'09), Montreal, Canada,2009,:633-640
81 Andrew P. Dempster, Nan M. Laird and Dark B. Rubin, Maximum Likelihood from Incomplete Data via the EM Algorithm, Journal of the Royal Statistical Society, Series B (Methodological),1977,39(1):1-38
82 Neal, Radford, Hinton, Geoffrey and Michael I. Jordan, A View of the EM Algorithm that Justifies Incremental, Sparse, and Other Variants, Learning in Graphical Models, Cambridge, MA:MIT Press,1999: 355-368
83庞博,计算机舌诊技术研究与系统实现,哈尔滨工业大学博士论文,2006：102-103
2 李乃民,中国舌诊大全,学苑出版社,1994,110-118
3 甄君,中西医对舌象的认识,辽宁中医学院学报,2000,2(3)：173
4 G. Maciocia, Tongue Diagnosis in Chinese Medicine, Seattle, WA: Eastland,1995,1-22
5 N.M. Li, et al, The Contemporary Investigations of Computerized Tongue Diagnosis, In:Li NM, eds, The Handbook of Chinese Tongue Diagnosis, Peking, Shed-Yuan Publishing,1994,1315-1317
6 B. Kirschbaum, Atlas of Chinese Tongue Diagnosis, Seattle, WA: Eastland,2000:110-118
7 朱丹溪,丹溪心法,中国医药科技出版社,2012：10-18
8 Eisenberg DM, Kessler RC, Foster C, Norlock FE, Calkins DR, Delbanco TL, Unconventional medicine in the United States:prevalence, costs, and patterns of use, The New England Journal of Medicine,1993,328(4): 246-52
9 Eisenberg DM, Davis RB, Ettner SL, Appel S, Wilkey S, Van Rompay M, et al, Trends in alternative medicine use in the United States 1097:results of a follow-up national survey, The Journal of the American Medical Association,1998,280(18):1569-75
10 Honda K, Jacobson JS, Use of complementary and alternative medicine among United States adults:the influences of personality, coping strategies, and social support, Preventive Medicine,2005,40(1):46-53
11 Thomas KJ, Nicholl JP, Coleman P, Use and expenditure on complementary medicine in England:a population based survey, Complementary Therapies Medicine,2001,9:2-11
12 Yamashita H, Tsukayama H, Sugishita C, Popularity of complementary and alternative medicine in Japan:a telephone survey, Complementary Therapies Medicine,2002,10:84-93
13 Leung PC, Charlie Changli Xue, Cheng YC, A comprehensive guide to Chinese medicine, World Scientific Ltd,1st ed, Singapore,2003
14 Yi Feng, Zhaohui Wu, Xuezhong Zhou, Zhongmei Zhou, Weiyu Fan, Knowledge discovery in traditional Chinese medicine:State of the art and perspectives, Artificial Intelligence in Medicine (2006) 38,219—236
15 E. Ernst, S, Mills, Complementary Medicine:A Definition, Br. J. Cen. Pract,1995,45:506
16 E. Ernst, The Role of Complementary and Alternative Medicine, BMJ, 2000,321:1133
17 David Zhang, Fengxi Song, Yong Xu, Zhizhen Liang, Advanced Biometric Recognition Technologies:Discriminant Criterion and Fusion Applications, In Press
18 D.Hoyer, K.Schmidt et.al, Nonlinear analysis of heart rate and respiratory dynamics, IEEE engineering in medicine and biology, Jan/Feb,1997, 31-39
19 Yoshio Maniwa, Tadashi Iokibe, Masaya Koyama, The Application of Pulse Wave Chaos in Clinical Medicine,17th Fuzzy System Symposium, 787-790
20 J. Kittler, M. Hatef, R. P. Duin, J. G. Matas, On combining classifiers, IEEE Transactions on PAMI 20 (3) (1998):226-239
21 E. Bigun, J. Bigun, B. Duc, S. Fischer, Expert conciliation for multi- modal person authentication systems using Bayesian Statistics, in:First International Conference on AVBPA, Crans-Montana, Switzerland, 1997,:291-300
22 T. K. Ho, J. J. Hull, S. N. Srihari, Decision combination in multiple classifier systems, IEEE Transactions on PAMI 16 (1) (1994):66-75
23 Naimin. Li, David. Zhang and Kuanquan. Wang, Tongue Diagnostics, Shed-Yuan Publishing,2006:78,:92-103
24中医药学名词审定委员会,中医药学名词,科学出版社,2005：12-13
25庄泽澄,中医诊断学,科学出版社,1999：27-37
26 Brent. Berlin and Paul Kay, Basic Color Terms:Their Universality and Evolution, Berkeley, CA:Univ. of California Press,1969
27 R.M. Boynton, Human Color Vision, Optical Society of America,1979
28 R.M. Evans, An Introduction to color, John Wiley & Sons,1948
29 Commission Internationale de l'Eclairage, No.15.2, In Colorimetry,2nd ed., volume 1, page 11, Vienna, Austria,1976
30 M.J.Vrhel, Color Imaging:Current Trends and Beyond, IEEE International Conference on Image Processing, Vancouver, BC, Canada,2000:513-516
31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,(14)
32张新峰,沈兰荪,卫保国,蔡轶珩,多类支撑向量机在中医舌质、舌苔分类和识别的应用研究,电路与系统学报,2004,(05)
33张新峰,沈兰荪,现代信息融合技术应用于中医舌象分析的初步研究,测控技术,2005,(05)
34 D. Zhang, B. Pang, N.M. Li, K.Q. Wang, H.Z. Zhang, Computerized Diagnosis from Tongue Appearance Using Quantitative Feature Classification, The American Journal of Chinese Medicine,2005,33(6): 859-866
35 Wenshu Li, Jianglong Xu,Changle Zhou, Zhifeng Zhang, A Novel Method of Tongue. Image Recognition, Proceedings of the Fifth International Conference on Computer and Information Technology, 2005,:586-590
36 B. Pang, D. Zhang, and K. Wang, Tongue image analysis for appendicitis diagnosis, Information Science, vol.175,:160-176,2005
37张新峰,沈兰荪,多特征融合技术应用于中医舌象分析的初步研究,电子学报,2006,(04)
38 H. Wang and X. Zong, A new computerized method for tongue classification, in Proc. IEEE Int. Conf. on Intelligent Systems Design and Applications,2006, vol.2,:508-511
39张衡翔,李斌,姚鹏,庄镇泉,岳小强,中医舌象自动分类方法研究,北京生物医学工程,2006,(01)
40 Y. Wang, J. Yang, Y. Zhou, and Y. Wang, Region partition and feature matching based color recognition of tongue image, Pattern Recognition Letter, vol.28 (1)::11-19, Jan.2007
41 Zhong Gao, Laiman Po, Wu Jiang, Xin Zhao, Hao Dong, A Novel Computerized Method Based on Support Vector Machine for Tongue Diagnosis, Third International IEEE Conference on Signal-Image Technologies and Internet-Based System,2007. SITIS'07.:849-854
42 Siu Cheung Hui, Yulan He, Doan Thi Cam Thach, Machine learning for tongue diagnosis,6th International Conference on Information, Communications & Signal Processing,2007,:1-5
43张萌,胡显伟,王元斌,张斌,AdaBoost算法在中医舌诊图像分区识别中的研究,小型微型计算机系统,2008,(06)
44 Lijun Jiang, Wang Xu, Jianfeng Chen, Digital imaging system for physiological analysis by tongue color inspection,3rd IEEE Conference on Industrial Electronics and Applications,2008, ICIEA,:1833-1836
45 Kim, K.H., Do, J.-H., Ryu, H., Kim, J.-Y., Tongue diagnosis method for extraction of effective region and classification of tongue coating, First Workshops on Image Processing Theory, Tools and Applications,2008, IPTA 2008,:1-7
46胡申宁,李文书,施国生,何芳芳,基于PCA-AdaBoost的舌象颜色分类研究,广西师范大学学报(自然科学版),2009,(03)
47 Yuke Wei, Jiangping Li, Qun Chen, Mei Liu, A new pattern recognition algorithm and its application about tongue fur image classification of Traditional Chinese Medicine,3rd International Conference on Biomedical Engineering and Informatics (BMEI),2010,:37-40
48 Weitong Huang, Zhaoqian Yan, Jiatuo Xu, Li Zhang, Analysis of the tongue fur and tongue features by naive Bayesian classifier, International Conference on Computer Application and System Modeling (ICCASM), 2010,:304-308
49 Yue Jiao, Xinfeng Zhang, Li Zhuo, Mingrui Chen, Kai Wang, Tongue image classification based on Universum SVM,3rd International Conference on Biomedical Engineering and Informatics (BMEI),2010, 657-660
50王爱民,沈兰荪,赵忠旭,监督FCM聚类算法及其在中医舌像自动分类中的应用,模式识别与人工智能,1999,(04)
51 Li, C.H, Yuen, P.C, Regularized color clustering in medical image database, IEEE Trans. Medical Imaging,2000,19 (11):1150-1155
52 Du Jian-qiang, Lu Yan-sheng, Zhang Kang, Zhu Ming-feng, Ding Cheng-hua, A Novel Approach of Tongue Body and Tongue Coating Separation Based on FCM, The 2nd International Conference on Bioinformatics and Biomedical Engineering IEEE,:2499-2503
53郭宙,杨学智,司银楚,朱庆文,牛欣,沙洪,基于K-均值聚类的常用色彩空间舌质舌苔分割研究,北京中医药大学学报,2009,(12)
54郭宙,杨学智,司银楚,朱庆文,牛欣,沙洪,一种基于K-均值聚类的CIELAB和HSI色彩空间舌质舌苔分割算法,中华中医药杂志,2010,(05)
55 J. H. Jang, J. E. Kim, K. M. Park, S. O. Park, Y. S. Chang, and B. Y. Kim, Development of digital tongue inspection system with image analysis, in Proc. IEEE EMBS/BMES,2002,:1033-1034
56余兴龙,竺子民,金国藩,谭耀麟,中医舌诊自动识别系统,仪器仪表学报,1994,(01)
57余兴龙,谭耀麟,竺子民,索忠堃,金国藩,翁维良,许秀森,葛文津,中医舌诊自动识别方法的研究,中国生物医学工程学报,1994,(04)
58樊艳,梁嵘,王召平,王立翔,数字舌图的色彩校正方法研究,世界科学技术-中医药现代化,2005,(04)
59陈松鹤,梁嵘,王盛花,王召平,黄庆梅,数字舌图的舌象颜色数据采集方法研究,世界科学技术-中医药现代化,2007,(02)
60徐晓昭,蔡轶珩,刘长江,贾克斌,沈兰荪,基于图像分析的偏色检测及颜色校正方法,测控技术,2008年05期
61徐晓昭,沈兰荪,刘长江,颜色校正方法及其在图像处理中的应用,计算机应用研究,2008年08期
62许家佗,张志枫,严竹娟,屠立平,陆璐明,史美育,朱凤兰,自然光条件下基于色差校正方法的舌象颜色分析,中西医结合学报2009,(05)
63许家佗,屠立平,张志枫,邱锡鹏,郭跃飞,危辉,一种基于LAB色空间拓扑剖分影射的颜色恒常性模型,电子学报,2009年09期
64晏峻峰,季梁,施诚,基于图像分析技术的开放式舌象研究平台的构建,医学信息,2004,(01)
65张晓丹,王忆勤,基于计算机技术的舌诊客观化检测仪器的临床应用概况,辽宁中医杂志,2009,(01)
66周纪芗,实用相关分析分析方法,上海科学技术出版社,(1990)
67唐启义、冯明光,实用统计分析及其DPS数据处理系统,科学出版社,北京,(2002)
68方开泰、马长兴,正交与均匀实验设计,科学出版社,北京,(2001)
69 Hickernell, F.J., Goodness-of-fit statistics, discrepancies and robust designs, Statist, Probab. Lett.,44,73-78, (1999)
70张宏志,舌数字图像获取与分析技术研究,哈尔滨工业大学博士论文,2007：12-13
71 Steve Gunn, Support Vector Machines for Classification and Regression, ISIS Technical Report,1998
72张学工,模式识别(第三版),北京：清华大学出版社,2000
73李晶皎,赵丽红,王爱侠,模式识别,北京：电子工业出版社,2010
74 Chen Li,Yap Mon-Tue,A fuzzy K-nearest-neighbour algorithm to blind image deconvolution, IEEE International Conference on Systems, Man and Cybernetics,2003
75 Xiaojin Zhu, Semi-Supervised Learning with Graphs, Ph.D dissertation, Dept. Electron. Eng., Carnegie Mellon University,2005
76 Y.-F. Li and Z.-H. Zhou, Improving semi-supervised support vector machines through unlabeled instances selection, In:Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI'11), San Francisco, CA,2011,:386-391
77 Y.-F. Li, J. Kwok, and Z.-H. Zhou, Cost-sensitive semi-supervised support vector machine, In:Proceedings of the 24th AAAI Conference on Artificial Intelligence (AAAI'10), Atlanta, GA,2010,:500-505
78 Z.-H. Zhou and M. Li, Semi-supervised learning by disagreement, Knowledge and Information Systems,2010,24(3):415-439
79 J.-M. Xu, G. Fumera, F. Roli, and Z.-H. Zhou. Training Spam Assassin with active semi-supervised learning, In:Proceedings of the 6th Conference on Email and Anti-Spam (CEAS'09), Mountain View, CA, 2009
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