面向中医舌诊的舌下静脉特征获取与分析
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摘要
舌诊是中医学中最常用和最有临床应用价值的诊法之一。借助迅速发展的数字图像处理和模式识别技术,开展自动化舌象获取与分析研究可以避免传统舌诊非量化和主观性的缺点,使中医舌诊为更广阔的领域所认可。至今为止,关于舌面特征提取的研究已经广泛开展,但本领域研究人员对中医舌诊的重要组成部分舌脉这一舌下络脉诊法的主要诊察指标的研究却甚少涉及。
本论文以面向中医诊断的舌下静脉的特征量化方法为研究中心,开展了舌下图像样本采集设备的研制、舌下静脉轮廓的分割和舌下静脉特征量化描述等方面的研究。
获取能有效描述舌下静脉特征的舌下图像信号是开展舌下静脉特征量化研究的基础。本论文以舌下络脉诊断中的舌脉诊断特征为考虑因素,首先从获取舌脉颜色特征的角度出发,应用彩色舌图像采集设备获取彩色舌下图像,并对所获取的图像进行了预处理。同时,鉴于彩色舌下图像描述舌脉距离信息特征的局限性,结合当前在生物特征识别领域广泛应用的静脉识别原理,系统地探讨了近红外舌下图像获取中的光源、传感器等基础问题,形成了可行的近红外舌下图像信号获取策略,并研制出一套新型近红外舌下图像信号采集系统原型。
然后,针对彩色舌下图像中舌脉与其周围舌质的对比度不同的特点,本论文分别提出了处理舌脉与舌质对比度较大的彩色舌下图像的基于像素聚类区域生长法的舌下静脉提取方法,以及针对舌脉与舌质对比度较小的彩色舌下图像的基于贝叶斯决策论的自适应舌脉轮廓提取方法。实验表明,所提出的方法针对舌脉与其周围舌质对比度的不同情况分别显现出较好的舌脉分割性能。
应用本论文研制的便携式近红外舌下静脉图像采集仪获取的近红外舌下静脉图像,较彩色舌下图像能够更真实地反映舌脉的生理轮廓。本论文针对所获取的图像的自身特点提出了基于近红外舌下图像的舌下静脉提取方法,为更准确地量化舌脉的距离信息特征奠定了基础。
在舌脉颜色特征的提取与量化方面,本论文以传统中医医师对舌脉颜色的判定结果为依据,构建了基于专家知识的舌脉颜色描述模型,进而给出了结合中医理论的舌脉颜色客观化描述;并应用基于KNN的像素级舌脉颜色分类方法在所构建的舌脉颜色模型基础上实现了舌脉颜色分类。实验结果表明,应用所提出的舌脉颜色模型及相应的舌脉颜色分类方法得到的舌脉颜色分类结果与中医诊断的舌脉颜色特征较为接近。
在舌脉距离信息特征方面,提出了用于测量舌脉的长度及阔度两个具有中医诊断价值的距离特征的测量方法,并对本论文所应用的两种舌下图像视觉信号,彩色舌下图像和近红外舌下图像,进行了舌脉区域距离信息的测量实验。
本论文在舌下图像信号的获取、舌下静脉轮廓提取以及舌下静脉特征量化等方面进行了系统的研究,使今后在舌诊客观化研究中应用多种富有诊断价值的舌下静脉特征辅助疾病的诊断成为可能。
Tongue diagnosis is one of the most common and valuable diagnostic method in Traditional Chinese Medicine. By means of the rapid development of digital image processing and pattern recognition technology, research on automatic tongue image acquisition and analysis can avoid the disadvantage of non-quantification and subjectivity in traditional tongue diagnosis, and make it widely acknowledged. Till now, research on feature extraction of tongue surface has been gradually perfect. However, research on one of the most important part of tongue diagnosis, the sublingual vein diagnosis, and its primary diagnosing indicator, the sublingual vein, is rarely referred by the researchers of this field.
This dissertation takes the feature quantification method of sublingual vein in traditional Chinese medicine as the research centre, carrying out researches including the design of sublingual image acquisition device, segmentation of sublingual vein and feature quantification of sublingual vein, respectively.
Acquiring sublingual image signal, which can effectively describe the feature of sublingual vein, is the basis of researching on the feature quantification of sublingual vein. This dissertation takes the diagnostic feature of sublingual vein in sublingual vein diagnosis into consideration. Firstly, from the viewpoint of acquiring the chromatic feature of sublingual vein, applying the color tongue image acquisition device to acquire the color sublingual image, and then preprocessing the acquired images. Meanwhile, whereas the limit of color sublingual image in describing the distance information feature of sublingual vein, combing with the vein recognition principle popularized in current Biometrics field, this dissertation systematically investigate the basic problem in near infrared sublingual image acquisition, such as the light source and sensor, etc., standardized acquisition strategy of near infrared sublingual image is formed. Finally, a novel near infrared sublingual image acquisition prototype is developed.
And then, focus on the different contrast between sublingual vein and surrounding tongue proper in color sublingual images, this dissertation proposes the pixel-clustering-based region growing algorithm for segmentation of sublingual veins from color sublingual image with visible contrast between sublingual vein and surrounding tongue proper, and the adaptive sublingual vein segmentation method based on Bayesian decision theory for color sublingual image with low contrast between sublingual vein and surrounding tongue proper, respectively. Experimental results reveal that, the proposed methods possess acceptable performance on sublingual vein segmentation for color sublingual image with different condition of contrast between sublingual vein and surrounding tongue proper.
The near infrared sublingual images acquired by using the portable near infrared sublingual vein image acquisition device designed in this dissertation, can more truly reflect the physiological contour of sublingual vein than the color sublingual images. Segmentation method of sublingual vein from the near infrared sublingual image is proposed in this dissertation according to the self characteristic of near infrared sublingual images, which establishes the foundation for quantifying the distance information feature of sublingual veins more precisely.
From the aspect of extraction and quantification of the color feature of sublingual veins, this dissertation takes the judgment results to the color of sublingual veins from Chinese physicians as the basis, establishes the color description model of sublingual vein based on expert knowledge, furthermore, proposes the objective description of the color of sublingual vein which take the theory of tongue diagnosis into consideration; moreover, pixel level classification of the color of sublingual vein based on KNN algorithm is applied to realize the classification of sublingual vein color on the basis of the established color description model of sublingual vein. Experimental results reveal that, the classification result of sublingual vein color obtained from the proposed model and the corresponding classification method are close to that given by Chinese physicians.
Considering the distance information of sublingual veins, measurement method of the length and width of sublingual veins, which both possess diagnostic value in tongue diagnosis, is proposed. And then, experiments are implemented to measure the distance information of the sublingual vein regions in two kinds of sublingual images, color sublingual image and near infrared sublingual image.
This dissertation systematically researches on the acquisition of sublingual images, segmentation of sublingual veins and feature quantification of sublingual veins, which make applying multiple sublingual vein features with diagnostic value to assist diseases diagnosing in the research on modernization of TCM tongue diagnosis come true in future.
本论文以面向中医诊断的舌下静脉的特征量化方法为研究中心,开展了舌下图像样本采集设备的研制、舌下静脉轮廓的分割和舌下静脉特征量化描述等方面的研究。
获取能有效描述舌下静脉特征的舌下图像信号是开展舌下静脉特征量化研究的基础。本论文以舌下络脉诊断中的舌脉诊断特征为考虑因素,首先从获取舌脉颜色特征的角度出发,应用彩色舌图像采集设备获取彩色舌下图像,并对所获取的图像进行了预处理。同时,鉴于彩色舌下图像描述舌脉距离信息特征的局限性,结合当前在生物特征识别领域广泛应用的静脉识别原理,系统地探讨了近红外舌下图像获取中的光源、传感器等基础问题,形成了可行的近红外舌下图像信号获取策略,并研制出一套新型近红外舌下图像信号采集系统原型。
然后,针对彩色舌下图像中舌脉与其周围舌质的对比度不同的特点,本论文分别提出了处理舌脉与舌质对比度较大的彩色舌下图像的基于像素聚类区域生长法的舌下静脉提取方法,以及针对舌脉与舌质对比度较小的彩色舌下图像的基于贝叶斯决策论的自适应舌脉轮廓提取方法。实验表明,所提出的方法针对舌脉与其周围舌质对比度的不同情况分别显现出较好的舌脉分割性能。
应用本论文研制的便携式近红外舌下静脉图像采集仪获取的近红外舌下静脉图像,较彩色舌下图像能够更真实地反映舌脉的生理轮廓。本论文针对所获取的图像的自身特点提出了基于近红外舌下图像的舌下静脉提取方法,为更准确地量化舌脉的距离信息特征奠定了基础。
在舌脉颜色特征的提取与量化方面,本论文以传统中医医师对舌脉颜色的判定结果为依据,构建了基于专家知识的舌脉颜色描述模型,进而给出了结合中医理论的舌脉颜色客观化描述;并应用基于KNN的像素级舌脉颜色分类方法在所构建的舌脉颜色模型基础上实现了舌脉颜色分类。实验结果表明,应用所提出的舌脉颜色模型及相应的舌脉颜色分类方法得到的舌脉颜色分类结果与中医诊断的舌脉颜色特征较为接近。
在舌脉距离信息特征方面,提出了用于测量舌脉的长度及阔度两个具有中医诊断价值的距离特征的测量方法,并对本论文所应用的两种舌下图像视觉信号,彩色舌下图像和近红外舌下图像,进行了舌脉区域距离信息的测量实验。
本论文在舌下图像信号的获取、舌下静脉轮廓提取以及舌下静脉特征量化等方面进行了系统的研究,使今后在舌诊客观化研究中应用多种富有诊断价值的舌下静脉特征辅助疾病的诊断成为可能。
Tongue diagnosis is one of the most common and valuable diagnostic method in Traditional Chinese Medicine. By means of the rapid development of digital image processing and pattern recognition technology, research on automatic tongue image acquisition and analysis can avoid the disadvantage of non-quantification and subjectivity in traditional tongue diagnosis, and make it widely acknowledged. Till now, research on feature extraction of tongue surface has been gradually perfect. However, research on one of the most important part of tongue diagnosis, the sublingual vein diagnosis, and its primary diagnosing indicator, the sublingual vein, is rarely referred by the researchers of this field.
This dissertation takes the feature quantification method of sublingual vein in traditional Chinese medicine as the research centre, carrying out researches including the design of sublingual image acquisition device, segmentation of sublingual vein and feature quantification of sublingual vein, respectively.
Acquiring sublingual image signal, which can effectively describe the feature of sublingual vein, is the basis of researching on the feature quantification of sublingual vein. This dissertation takes the diagnostic feature of sublingual vein in sublingual vein diagnosis into consideration. Firstly, from the viewpoint of acquiring the chromatic feature of sublingual vein, applying the color tongue image acquisition device to acquire the color sublingual image, and then preprocessing the acquired images. Meanwhile, whereas the limit of color sublingual image in describing the distance information feature of sublingual vein, combing with the vein recognition principle popularized in current Biometrics field, this dissertation systematically investigate the basic problem in near infrared sublingual image acquisition, such as the light source and sensor, etc., standardized acquisition strategy of near infrared sublingual image is formed. Finally, a novel near infrared sublingual image acquisition prototype is developed.
And then, focus on the different contrast between sublingual vein and surrounding tongue proper in color sublingual images, this dissertation proposes the pixel-clustering-based region growing algorithm for segmentation of sublingual veins from color sublingual image with visible contrast between sublingual vein and surrounding tongue proper, and the adaptive sublingual vein segmentation method based on Bayesian decision theory for color sublingual image with low contrast between sublingual vein and surrounding tongue proper, respectively. Experimental results reveal that, the proposed methods possess acceptable performance on sublingual vein segmentation for color sublingual image with different condition of contrast between sublingual vein and surrounding tongue proper.
The near infrared sublingual images acquired by using the portable near infrared sublingual vein image acquisition device designed in this dissertation, can more truly reflect the physiological contour of sublingual vein than the color sublingual images. Segmentation method of sublingual vein from the near infrared sublingual image is proposed in this dissertation according to the self characteristic of near infrared sublingual images, which establishes the foundation for quantifying the distance information feature of sublingual veins more precisely.
From the aspect of extraction and quantification of the color feature of sublingual veins, this dissertation takes the judgment results to the color of sublingual veins from Chinese physicians as the basis, establishes the color description model of sublingual vein based on expert knowledge, furthermore, proposes the objective description of the color of sublingual vein which take the theory of tongue diagnosis into consideration; moreover, pixel level classification of the color of sublingual vein based on KNN algorithm is applied to realize the classification of sublingual vein color on the basis of the established color description model of sublingual vein. Experimental results reveal that, the classification result of sublingual vein color obtained from the proposed model and the corresponding classification method are close to that given by Chinese physicians.
Considering the distance information of sublingual veins, measurement method of the length and width of sublingual veins, which both possess diagnostic value in tongue diagnosis, is proposed. And then, experiments are implemented to measure the distance information of the sublingual vein regions in two kinds of sublingual images, color sublingual image and near infrared sublingual image.
This dissertation systematically researches on the acquisition of sublingual images, segmentation of sublingual veins and feature quantification of sublingual veins, which make applying multiple sublingual vein features with diagnostic value to assist diseases diagnosing in the research on modernization of TCM tongue diagnosis come true in future.
引文
1李乃民.中国舌诊大全.学苑出版社. 1994
2李乃民.急腹症舌像图谱.黑龙江科学技术出版社. 1987:31~37
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李莱田,田道正,焦春荣.全息医学大全.中国医药科技出版社. 1997: 1~28, 35~48, 171~180
5李圣春,凌昌全.计算机技术在中医舌诊研究中应用进展.安徽中医学院学报. 2001, 20(6): 58~60
6 C.C. Yang, W.W.M. Chan. Metadata Design for Chinese Medicine Digital Library Using XML. Proceedings of the 33rd Annual Hawaii International Conference on System Sciences. 2000: 1627~1636
7 S. Lobregt, M.A. Viergever. A Discrete Dynamic Contour Model. IEEE Trans. On Medical Imaging. 1995, 14(1): 12~24
8佘娅.数字式舌诊仪的研制与应用.计算技术与自动化. 1990, 9(1): 68~72
9赵荣莱,危北海,丁瑞,郭培元等.舌质舌苔的计算机定量描述和分类.中医杂志. 1989, 30(2): 47~48
10孙立有.利用计算机图像识别技术进行舌诊客观化研究的探讨.安徽中医学院学报. 1986, 5(4): 5~7
11 Shen Lansun et al. Image Analysis for Tongue Characterization. Chinese Journal of Electronics. 2003, 12(3): 317~323
12张伯礼,徐宗佩,刘华一,张金英,高秀梅,毛秉豫.舌象色度学研究.天津中医.1992, 9(4): 38~40
13张伯礼.中医舌诊客观化系列研究.天津中医学院学报. 1992, (4): 34~38
14 W.L. Weng. The Development of Modern Tongue Diagnosis. In: Weng WL,eds. The Illustrations of Clinical Tongue Diagnosis and Disease Treatments. Peking, Shed-Yuan Publishing, 1997: 26~49
15许家佗,方肇勤,张志枫.舌象客观化识别方法的研究进展.上海中医药杂志. 2002, (2): 42~45
16秦吉华.舌诊客观化检测仪器的新进展.中医药动态. 1993, (1): 13
17 Y. Cai. A Novel Imaging System for Tongue Inspection. Proceedings of the 19th IEEE Conference on Instrumentation and Measurement Technology (IMTC/2002). 2002, 1:159~163
18蔡轶珩,刘长江,沈兰荪.新型舌象分析仪的设计方案.测控技术. 2005, 24(5): 34~36
19李庆利,薛永祺,王建宇,岳小强.基于超光谱图像的舌体分割算法.红外与毫米波学报. 2007,26(1): 77~80
20李庆利,薛永祺,王建宇,岳小强.高光谱成像系统在中医舌诊中的应用研究.红外与毫米波学报. 2006, 25(6): 465~468
21赵忠旭,等.基于数学形态学和HIS模型的色彩舌图像分割.北京工业大学学报. 1999, 25(2): 67~71
22王永刚.中医舌像分析仪的实用化研究.北京工业大学硕士学位论文. 2001
23 Bo Pang, et al. On Automated Tongue Image Segmentation in Chinese Medicine. Pattern Recognition, 2002. Proceedings 16th international Conference on, Volume 1: 616~619
24朱洁华,阮邦志,励俊雄,邝枣园,吴伟.舌诊客观化研究的一种图像处理方法.中国生物医学工程学报. 2001, 20(2): 132~137
25 G. Li, Y. Cai. Texture analysis for tongue analysis. Technical Report BV-2003-2,School of Computer Science, Carnegie Mellon University, May 2003
26周越,沈利,杨杰.基于图像处理的中医舌像特征分析方法.红外与激光工程.2002, 31(6): 490~494
27李庆利,薛永祺,刘治,岳小强.基于高光谱成像技术的中医舌纹分析算法.光电工程. 2007, 34(4):60~64
28钟少丹,韦玉科,谢铮桂.齿痕舌像自动分割的方法.计算机技术与发展. 2009, 19(1): 245~247
29蒋依吾,陈建仲,张恒鸿,马建中.电脑化中医舌诊系统.中国中西医结合杂志.2000, 20(2): 145~147
30 A.M. Wang, L.S. Shen, Z.X. Zhao. Fuzzy Automatic Detecting the Thickness of Tongue-Covering from a Tongue Image. Proceedings of the 4th International Conference on Electronic Measurement and Instruments, Harbin, China. 1999:863~867
31 P.C. Yuen, Z.Y. Kuang, W. Wu, Y.T. Wu. Tongue Texture Analysis UsingOpponent Color Features for Tongue Diagnosis in Traditional Chinese Medicine. Proceedings of International Workshop on Texture Analysis in Machine Vision. 1999: 21~27
32 C.C. Chiu, T.S. Cheng, Y.H. Chang. The quantitative analysis for the textural properties of tongue coating based on hierarchical clustering approaches. Proceedings of the Biomedical Engineering Society 1997 Annual Symposium. 1997: 126~127
33 C.C. Chiu, S.F. Hsu, W.S. Chen. An initial study of quantitative analysis on tongue images. Proceedings of the Biomedical Engineering Society 1995 Annual Symposium. 1995: 33~34
34 C.C. Chiu, H.S. Lin, S.L. Lin. A Structural Texture Recognition Approach for Medical Diagnosis through Tongue. Biomedical Engineering Applications, Basis Communications. 1995, 7(2): 14~19
35 C.H. Li, P.C. Yuen. Normalized Sampling for Color Clustering in Medical Diagnosis. Proceeding of the 16th International Conference on Pattern Recognition, Quebec City, QC, Canada. 2002, 3: 819~822
36 C.H. Li, P.C. Yuen. Regularized Color Clustering in Medical Image Database. IEEE Transactions on Medical Imaging. 2000, 19(11): 1150~1155
37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(3): 136~141
38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, 12(4): 480~485
39 C.H. Li, P.C. Yuen. Tongue Image Matching Using Color Content. Pattern Recognition.2002, 35(2): 407~419
40苏开娜,卢翔飞.基于图像处理的舌苔润燥分析方法的研究.中国图像图形学报.1999, 4A(4): 345~348
41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, 9(5): 110~113
42杜军平,涂序彦.计算机图像处理技术在舌像特征提取中的应用.中国医学影像技术. 2003, 19(204): 150~152
43 Z.X. Zhao, A.M. Wang, L.S. Shen. An Automatic Tongue Analyzer of Chinese Medicine Based on Color Image Processing. Proceedings of the 4th International Conference on Electronic Measurement and Instruments, Harbin, China.1999:830~834
44 Y.H. Chang, C.C. Chiu, T.S. Cheng. The quantitative analysis of tongue color for healthy persons. M. Taiwan J. Med. 1998, 1: 47~52
45 C.C. Chiu, Y.H. Chang, T.S. Cheng. The insensitivity analysis of lighting system and chromaticity analysis of normal subjects for computerized tongue diagnosis system. Proceedings of the 10th IPPR Conference on Computer Vision, Graphics, and Image Processing. 1997: 15~19
46 H.S. Lin, C.C. Chiu, C.Y. Lin, S.F. Hsu. Algorithms for determining chromatic and textural properties of tongue images. Proceedings of the 8th IPPR Conference on Computer Vision, Graphics, and Image Processing. 1995: 70~77
47尚瑞梅.舌体大小的临床意义与客观化计量化研究.浙江中医杂志. 1993, (11): 518
48尚瑞梅,等.智能化舌体(形)测算仪的研制及应用──附1733例患者检测初步分析. 1994, 14(8): 497~499
49王俊茹,等. 519例恶性肿瘤患者舌体测量和临床意义初探.全国中西医结合肿瘤学术研讨会论文集,长沙, 1991: 102
50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
51卫保国,沈兰荪.舌体胖瘦的自动分析.计算机工程. 2004, 30(11): 25~26
52 B. Pham, Y. Cai. Visualization Techniques for Tongue Analysis in Traditional Chinese Medicine. Galloway, Robert L., Jr. Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display, San Diego, California, USA. Proceedings of the SPIE, 2004, 5367: 171~180
53 T. Watsuji, S. Arita, S. Shinohara, T. Kitade. Medical Application of Fuzzy Theory to the Diagnostic System of Tongue Inspection in Traditional Chinese Medicine. Proceedings of IEEE International Conference on Fuzzy Systems, Seoul, Korea. 1999, 1: 145~148
54 T. Watsuji, S. Arita. Estimation for Diagnostic Information of the Tongue Inspection Using a Fuzzy Theory. Proceedings of the 9th Annual Meeting of Biomedical Fuzzy Systems Association. 1996: 86~89
55洪尚杓.血瘀证舌诊定量研究.中国中医研究院博士学位论文. 2000
56 J.H. Jang, J.E. Kim, K.M. Park, S.O. Park, Y.S. Chang, B.Y. Kim, Development of the Digital Tongue Inspection System with Image Analysis. Proceedings ofthe Second Joint EMBS/BMES Conference. 2002, 2: 1033~1034
57竺子民,余兴龙,金国藩,谭耀麟.中医舌像自动识别系统的研究.华中理工大学学报. 1995, 23(8): 55~59
58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, 13(4): 336~344
59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. 1994,15(1): 67~72
60 C.C. Chiu. A Novel Approach Based on Computerized Image Analysis for Traditional Chinese Medical Diagnosis of the Tongue. Computer Methods and Programs in Biomedicine. 2000, 61(2): 77~89
61 C.C. Chiu. Development of a Computerized Tongue Diagnosis System. Biomedical Engineering Applications, Basis Communications. 1996, 8(4): 342~350
62翁维良,黄世敬.中医舌诊客观化研究.中国工程科学. 2001, 3(1): 78~82
63翁维良等.中医舌象真彩色图像系统的研制与应用.实用中西医结合杂志. 1995, 8(24): 1655
64王爱民.用于舌诊客观化的图像分析技术的研究.北京工业大学博士学位论文.2001
65王永刚,王爱民,沈兰荪.舌象分析仪中舌色重现方法的研究.照明工程学报.2001, 12(2): 4~10
66王永刚.中医舌象分析仪的实用化研究.北京工业大学硕士学位论文. 2001
67赵忠旭.中医舌象分析关键技术的研究与科研样机的实现.北京工业大学博士学位论文. 1999
68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): 164~166, 169
69沈兰荪,赵忠旭,王爱民,王永刚.基于图像处理的中医舌像分析仪的研究.国外电子测量技术. 1999, (6): 33~35
70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—中医药现代化. 2003, 5(1): 15~19
71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
72崔敏圭,顼宝玉,黄世敬,翁维良.中风病舌诊定量研究.中国中西医结合杂志.2001, 21(9): 670~673
73石行让,尚瑞梅.舌体的定量化研究.系统工程理论与实践. 1997, (2): 109~112
74梁嵘,张永涛.建立数字舌图信息库是进行中医诊断学理论创新的基础.中国医学影像技术. 2003, 19(204): 116~117
75 Bo Pang, David Zhang, Naimin Li, Kuanquan Wang. Computerized tongue diagnosis based on Bayesian networks. IEEE Transactions on Biomedical Engineering,2004,51(10),1803~1810
76 M. Takeichi, T. Sato. Computerized Color Analysis of“Xue Yu”(Blood Stasis) in the Sublingual Vein Using a New Technology. Sublingual Vein Using a New Technology. American Journal of Chinese Medicine.1997, 25(2): 213~219
77 M. Takeichi, T. Sato. Studies on the psychosomatic functioning of ill-health according to Eastern and Western medicine. Am. J. Chinese Med.. 1999, 27: 213~219
78 C. C. Chiu, et al., Objective assessment of blood stasis using computerized inspection of sublingual veins. Computer Methods and Programs in Biomedicine. 2002, 69:1~12
79陈泽霖,陈梅芳.舌诊研究.第2版.上海:上海科学技术出版社, 1982:73
80张赞臣.舌下望诊法.中医杂志,1964, 2:28
81上海第一医学院.医学情况交流. 1977, 1:13
82李寿山.初论舌下络脉诊法在临床上的应用.新中医. 1982(5):4~6
83周学海.形色外诊简摩.北京:人民卫生出版社. 1987, 90
84今田屋章.提倡舌背诊.国外医学中医中药分册. 1981(4):58
85三谷和合.舌诊证.东洋医学(日). 1992, 20(2):19~24
86 Takeichi M, Sato T. Studies on the psychosomatic function of ill-health according to eastern and Western medicine. 1. Visual observation of the sublingual vein for early detection of vital energy stagnation and blood stasion. American Journal of Chinese Medicine, 1999, 27(1):43~51
87腾本莲风.舌诊的有用性——1例白血病的观察.针灸(日). 2003, 19(2):201~209
88张宏志.舌图像获取与分析技术研究.哈尔滨工业大学博士学位论文. 2007
89 M. Kass, A. Witkin, and D. Terzopoulos, Snakes: active contour models, Proc. Int. Conf. Computer Vision. 1987: 259~268
90 H.D. Zeman,G. Lovhoiden, and H. Deshmukh. Design of a clinical vein contrast enhancing projector. in Biomedical Diagnostic,Guidance,and Surgical-Assist Systems III. 2001.San Jose,CA,USA:SPIE.
91 G. Lovhoiden, H. Deshmukh,and H.D. Zeman. Clinical evaluation of vein contrast enhancement. in Biomedical Diagnostic,Guidance,and Surgical-Assist Systems IV. 2002. San Jose,CA,USA:SPIE.
92 K. Shimizu, Optical trans-body imaging: feasibility of non invasion CT and functional imaging of living body. Medicina Philosophica,1992.11:620~629
93 H. D. Zeman, L. Gunnar. Enhancing the Contrast of Subcutaneous Vein. Proc, SPIE,1999, 3595: 219~230
94 H. D. Zeman, L. Gunnar, H. Deshmuk. Optimization of Subcutaneous Vein Contrast Enhancement. Proc.SPIE, 2000, 3911: 50~57
95 H. D. Zeman, L. Gunnar, C. Vrancken. Prototype Vein Contrast Enhancer. Proc, SPIE, 2004,5318(1):39~49
96 M. Kono, H. Ueki, S.I. Umemura, Near-infrared finger vein patterns for personal identification. Applied Optics, 2002. 41(35):7429~7436
97 S.K. Im, H.S.Choi, S.W.Kim, A Direction-Based Vascular Pattern Extraction Algorithm for Hand Vascular Pattern Verification ETRI Journal, 2000. 25(2):101~108
98 Im,S.K.,et al.,An Biometric Identification System by Extracting Hand Vein Patterns. Journal of the Korean Physical Society, 2001, 38(3): 268~272
99 Tanaka,T.and N.Kubo.Biometric authentication by hand vein patterns.in SICE 2004 Annual Conference.2004
100 J.M. Cross,C.L.Smith. Thermographic imaging of the subcutaneous vascular network of the back of the hand for verification of identity. In Proceedings. Institute of Electrical and Electronics Engineers 29th Annual 1995 International Carnahan Conference on Security Technology.1995
101 A.M. Badawi, Hand Vein Biometric Verification Prototype: A Testing Performance and Patterns Similarity, in Proceedings of The 2006 International Conference on Image Processing, Computer Vision, and Pattern Recognition. 2006: Las Vegas, USA.
102 L Wang, L. Graham, A thermal hand vein pattern verification system, in PatternRecognition and Image Analysis, S.Singh, et al., Editors. 2005, Springer Berlin/Heidelberg. 58~65
103 M Shahin, A Badawi, M Kamel. Biometric Authentication Using Fast Correlation of Near Infrared Hand Vein Patterns. International Journal of Biomedical Sciences, 2(3):141~148
104 A N Bashkatov, E A Genina, V I Kochubey, et al.Optical properties of human skin, subcutaneous and mucous tissues in the wavelength range from 400 to 2000nm, J. Phys.D:Appl.Phys,38(2006):2543~2555
105杜培胜,郭靖,董前民.液晶可调谐光片及其在光谱仪上的应用.红外,2007,28(11):4~8
106韩笑.人体手背静脉识别算法研究.吉林大学博士学位论文. 2007
107 A. Hoover, V. Kouznetsova, M. Goldbaum. Locating Blood Vessels in Retinal Images by Piecewise Threshold Probing of a Matched Filter Response. IEEE Trans. Med. Imag.. 2000,19(3):203~210
108 S. Chaudhuri, S. Chatterjee, N. Katz, et al. Detection of Blood Vessels in Retinal images Using Two-Dimensional Matched Filters. IEEE Trans. Med. Imag.. 1989,8(3):263~269
109 S. Tamura, K. Tanaka, S. Ohmori, K. Okazaki, A. Okada, M. Hoshi. Semiautomatic Leakage Analyzing System for Time Series Fluorescein Ocular Fundus Angiography. Pattern Recognition. 1983,16(2):149~162
110 Liang Zhou, Mark S. Rzeszotarski, Lawrence J. Singerman, et al. The Detection and Quantification of Retinopathy Using Digital Angiograms. IEEE Trans. Med. Imag.. 1994, 13(4): 619~625
111 Mark D.Fairchild. Color Appearance Models. Addison Wesley, Reading, Massachussets, 1998
112 Charles A.Poynton. Frequently Asked Questions about Color. http://www.inforamp.net/~poynton.
113 Hardie, R. C., Boncelet, C.. LUM Filters: A Class of Rank-Order-Based Filters for Smoothing and Sharpening. IEEE Transactions on Signal Processing. 1993,41(3):1061~1076
114 L. Vincent, P. Soille. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Patt. Anal. Mach. Intell. 1991,13(6):583~598
115 J Bernsen. Dynamic thresholding of gray-level images. Proceedings of the 8thInternational Conference on Pattern Recognition. Paris: IEEE Computer Society Press, 1986
116李剑锋.面向中医诊断的自动舌图像处理系统.哈尔滨工业大学硕士论文. 2002
117郭振华.舌图像的颜色分析.哈尔滨工业大学硕士论文. 2004
118边肇祺,张学工.模式识别.清华大学. 2000,1(2):136~140,235~237
119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
2李乃民.急腹症舌像图谱.黑龙江科学技术出版社. 1987:31~37
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李莱田,田道正,焦春荣.全息医学大全.中国医药科技出版社. 1997: 1~28, 35~48, 171~180
5李圣春,凌昌全.计算机技术在中医舌诊研究中应用进展.安徽中医学院学报. 2001, 20(6): 58~60
6 C.C. Yang, W.W.M. Chan. Metadata Design for Chinese Medicine Digital Library Using XML. Proceedings of the 33rd Annual Hawaii International Conference on System Sciences. 2000: 1627~1636
7 S. Lobregt, M.A. Viergever. A Discrete Dynamic Contour Model. IEEE Trans. On Medical Imaging. 1995, 14(1): 12~24
8佘娅.数字式舌诊仪的研制与应用.计算技术与自动化. 1990, 9(1): 68~72
9赵荣莱,危北海,丁瑞,郭培元等.舌质舌苔的计算机定量描述和分类.中医杂志. 1989, 30(2): 47~48
10孙立有.利用计算机图像识别技术进行舌诊客观化研究的探讨.安徽中医学院学报. 1986, 5(4): 5~7
11 Shen Lansun et al. Image Analysis for Tongue Characterization. Chinese Journal of Electronics. 2003, 12(3): 317~323
12张伯礼,徐宗佩,刘华一,张金英,高秀梅,毛秉豫.舌象色度学研究.天津中医.1992, 9(4): 38~40
13张伯礼.中医舌诊客观化系列研究.天津中医学院学报. 1992, (4): 34~38
14 W.L. Weng. The Development of Modern Tongue Diagnosis. In: Weng WL,eds. The Illustrations of Clinical Tongue Diagnosis and Disease Treatments. Peking, Shed-Yuan Publishing, 1997: 26~49
15许家佗,方肇勤,张志枫.舌象客观化识别方法的研究进展.上海中医药杂志. 2002, (2): 42~45
16秦吉华.舌诊客观化检测仪器的新进展.中医药动态. 1993, (1): 13
17 Y. Cai. A Novel Imaging System for Tongue Inspection. Proceedings of the 19th IEEE Conference on Instrumentation and Measurement Technology (IMTC/2002). 2002, 1:159~163
18蔡轶珩,刘长江,沈兰荪.新型舌象分析仪的设计方案.测控技术. 2005, 24(5): 34~36
19李庆利,薛永祺,王建宇,岳小强.基于超光谱图像的舌体分割算法.红外与毫米波学报. 2007,26(1): 77~80
20李庆利,薛永祺,王建宇,岳小强.高光谱成像系统在中医舌诊中的应用研究.红外与毫米波学报. 2006, 25(6): 465~468
21赵忠旭,等.基于数学形态学和HIS模型的色彩舌图像分割.北京工业大学学报. 1999, 25(2): 67~71
22王永刚.中医舌像分析仪的实用化研究.北京工业大学硕士学位论文. 2001
23 Bo Pang, et al. On Automated Tongue Image Segmentation in Chinese Medicine. Pattern Recognition, 2002. Proceedings 16th international Conference on, Volume 1: 616~619
24朱洁华,阮邦志,励俊雄,邝枣园,吴伟.舌诊客观化研究的一种图像处理方法.中国生物医学工程学报. 2001, 20(2): 132~137
25 G. Li, Y. Cai. Texture analysis for tongue analysis. Technical Report BV-2003-2,School of Computer Science, Carnegie Mellon University, May 2003
26周越,沈利,杨杰.基于图像处理的中医舌像特征分析方法.红外与激光工程.2002, 31(6): 490~494
27李庆利,薛永祺,刘治,岳小强.基于高光谱成像技术的中医舌纹分析算法.光电工程. 2007, 34(4):60~64
28钟少丹,韦玉科,谢铮桂.齿痕舌像自动分割的方法.计算机技术与发展. 2009, 19(1): 245~247
29蒋依吾,陈建仲,张恒鸿,马建中.电脑化中医舌诊系统.中国中西医结合杂志.2000, 20(2): 145~147
30 A.M. Wang, L.S. Shen, Z.X. Zhao. Fuzzy Automatic Detecting the Thickness of Tongue-Covering from a Tongue Image. Proceedings of the 4th International Conference on Electronic Measurement and Instruments, Harbin, China. 1999:863~867
31 P.C. Yuen, Z.Y. Kuang, W. Wu, Y.T. Wu. Tongue Texture Analysis UsingOpponent Color Features for Tongue Diagnosis in Traditional Chinese Medicine. Proceedings of International Workshop on Texture Analysis in Machine Vision. 1999: 21~27
32 C.C. Chiu, T.S. Cheng, Y.H. Chang. The quantitative analysis for the textural properties of tongue coating based on hierarchical clustering approaches. Proceedings of the Biomedical Engineering Society 1997 Annual Symposium. 1997: 126~127
33 C.C. Chiu, S.F. Hsu, W.S. Chen. An initial study of quantitative analysis on tongue images. Proceedings of the Biomedical Engineering Society 1995 Annual Symposium. 1995: 33~34
34 C.C. Chiu, H.S. Lin, S.L. Lin. A Structural Texture Recognition Approach for Medical Diagnosis through Tongue. Biomedical Engineering Applications, Basis Communications. 1995, 7(2): 14~19
35 C.H. Li, P.C. Yuen. Normalized Sampling for Color Clustering in Medical Diagnosis. Proceeding of the 16th International Conference on Pattern Recognition, Quebec City, QC, Canada. 2002, 3: 819~822
36 C.H. Li, P.C. Yuen. Regularized Color Clustering in Medical Image Database. IEEE Transactions on Medical Imaging. 2000, 19(11): 1150~1155
37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(3): 136~141
38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, 12(4): 480~485
39 C.H. Li, P.C. Yuen. Tongue Image Matching Using Color Content. Pattern Recognition.2002, 35(2): 407~419
40苏开娜,卢翔飞.基于图像处理的舌苔润燥分析方法的研究.中国图像图形学报.1999, 4A(4): 345~348
41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, 9(5): 110~113
42杜军平,涂序彦.计算机图像处理技术在舌像特征提取中的应用.中国医学影像技术. 2003, 19(204): 150~152
43 Z.X. Zhao, A.M. Wang, L.S. Shen. An Automatic Tongue Analyzer of Chinese Medicine Based on Color Image Processing. Proceedings of the 4th International Conference on Electronic Measurement and Instruments, Harbin, China.1999:830~834
44 Y.H. Chang, C.C. Chiu, T.S. Cheng. The quantitative analysis of tongue color for healthy persons. M. Taiwan J. Med. 1998, 1: 47~52
45 C.C. Chiu, Y.H. Chang, T.S. Cheng. The insensitivity analysis of lighting system and chromaticity analysis of normal subjects for computerized tongue diagnosis system. Proceedings of the 10th IPPR Conference on Computer Vision, Graphics, and Image Processing. 1997: 15~19
46 H.S. Lin, C.C. Chiu, C.Y. Lin, S.F. Hsu. Algorithms for determining chromatic and textural properties of tongue images. Proceedings of the 8th IPPR Conference on Computer Vision, Graphics, and Image Processing. 1995: 70~77
47尚瑞梅.舌体大小的临床意义与客观化计量化研究.浙江中医杂志. 1993, (11): 518
48尚瑞梅,等.智能化舌体(形)测算仪的研制及应用──附1733例患者检测初步分析. 1994, 14(8): 497~499
49王俊茹,等. 519例恶性肿瘤患者舌体测量和临床意义初探.全国中西医结合肿瘤学术研讨会论文集,长沙, 1991: 102
50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
51卫保国,沈兰荪.舌体胖瘦的自动分析.计算机工程. 2004, 30(11): 25~26
52 B. Pham, Y. Cai. Visualization Techniques for Tongue Analysis in Traditional Chinese Medicine. Galloway, Robert L., Jr. Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display, San Diego, California, USA. Proceedings of the SPIE, 2004, 5367: 171~180
53 T. Watsuji, S. Arita, S. Shinohara, T. Kitade. Medical Application of Fuzzy Theory to the Diagnostic System of Tongue Inspection in Traditional Chinese Medicine. Proceedings of IEEE International Conference on Fuzzy Systems, Seoul, Korea. 1999, 1: 145~148
54 T. Watsuji, S. Arita. Estimation for Diagnostic Information of the Tongue Inspection Using a Fuzzy Theory. Proceedings of the 9th Annual Meeting of Biomedical Fuzzy Systems Association. 1996: 86~89
55洪尚杓.血瘀证舌诊定量研究.中国中医研究院博士学位论文. 2000
56 J.H. Jang, J.E. Kim, K.M. Park, S.O. Park, Y.S. Chang, B.Y. Kim, Development of the Digital Tongue Inspection System with Image Analysis. Proceedings ofthe Second Joint EMBS/BMES Conference. 2002, 2: 1033~1034
57竺子民,余兴龙,金国藩,谭耀麟.中医舌像自动识别系统的研究.华中理工大学学报. 1995, 23(8): 55~59
58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, 13(4): 336~344
59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. 1994,15(1): 67~72
60 C.C. Chiu. A Novel Approach Based on Computerized Image Analysis for Traditional Chinese Medical Diagnosis of the Tongue. Computer Methods and Programs in Biomedicine. 2000, 61(2): 77~89
61 C.C. Chiu. Development of a Computerized Tongue Diagnosis System. Biomedical Engineering Applications, Basis Communications. 1996, 8(4): 342~350
62翁维良,黄世敬.中医舌诊客观化研究.中国工程科学. 2001, 3(1): 78~82
63翁维良等.中医舌象真彩色图像系统的研制与应用.实用中西医结合杂志. 1995, 8(24): 1655
64王爱民.用于舌诊客观化的图像分析技术的研究.北京工业大学博士学位论文.2001
65王永刚,王爱民,沈兰荪.舌象分析仪中舌色重现方法的研究.照明工程学报.2001, 12(2): 4~10
66王永刚.中医舌象分析仪的实用化研究.北京工业大学硕士学位论文. 2001
67赵忠旭.中医舌象分析关键技术的研究与科研样机的实现.北京工业大学博士学位论文. 1999
68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): 164~166, 169
69沈兰荪,赵忠旭,王爱民,王永刚.基于图像处理的中医舌像分析仪的研究.国外电子测量技术. 1999, (6): 33~35
70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—中医药现代化. 2003, 5(1): 15~19
71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
72崔敏圭,顼宝玉,黄世敬,翁维良.中风病舌诊定量研究.中国中西医结合杂志.2001, 21(9): 670~673
73石行让,尚瑞梅.舌体的定量化研究.系统工程理论与实践. 1997, (2): 109~112
74梁嵘,张永涛.建立数字舌图信息库是进行中医诊断学理论创新的基础.中国医学影像技术. 2003, 19(204): 116~117
75 Bo Pang, David Zhang, Naimin Li, Kuanquan Wang. Computerized tongue diagnosis based on Bayesian networks. IEEE Transactions on Biomedical Engineering,2004,51(10),1803~1810
76 M. Takeichi, T. Sato. Computerized Color Analysis of“Xue Yu”(Blood Stasis) in the Sublingual Vein Using a New Technology. Sublingual Vein Using a New Technology. American Journal of Chinese Medicine.1997, 25(2): 213~219
77 M. Takeichi, T. Sato. Studies on the psychosomatic functioning of ill-health according to Eastern and Western medicine. Am. J. Chinese Med.. 1999, 27: 213~219
78 C. C. Chiu, et al., Objective assessment of blood stasis using computerized inspection of sublingual veins. Computer Methods and Programs in Biomedicine. 2002, 69:1~12
79陈泽霖,陈梅芳.舌诊研究.第2版.上海:上海科学技术出版社, 1982:73
80张赞臣.舌下望诊法.中医杂志,1964, 2:28
81上海第一医学院.医学情况交流. 1977, 1:13
82李寿山.初论舌下络脉诊法在临床上的应用.新中医. 1982(5):4~6
83周学海.形色外诊简摩.北京:人民卫生出版社. 1987, 90
84今田屋章.提倡舌背诊.国外医学中医中药分册. 1981(4):58
85三谷和合.舌诊证.东洋医学(日). 1992, 20(2):19~24
86 Takeichi M, Sato T. Studies on the psychosomatic function of ill-health according to eastern and Western medicine. 1. Visual observation of the sublingual vein for early detection of vital energy stagnation and blood stasion. American Journal of Chinese Medicine, 1999, 27(1):43~51
87腾本莲风.舌诊的有用性——1例白血病的观察.针灸(日). 2003, 19(2):201~209
88张宏志.舌图像获取与分析技术研究.哈尔滨工业大学博士学位论文. 2007
89 M. Kass, A. Witkin, and D. Terzopoulos, Snakes: active contour models, Proc. Int. Conf. Computer Vision. 1987: 259~268
90 H.D. Zeman,G. Lovhoiden, and H. Deshmukh. Design of a clinical vein contrast enhancing projector. in Biomedical Diagnostic,Guidance,and Surgical-Assist Systems III. 2001.San Jose,CA,USA:SPIE.
91 G. Lovhoiden, H. Deshmukh,and H.D. Zeman. Clinical evaluation of vein contrast enhancement. in Biomedical Diagnostic,Guidance,and Surgical-Assist Systems IV. 2002. San Jose,CA,USA:SPIE.
92 K. Shimizu, Optical trans-body imaging: feasibility of non invasion CT and functional imaging of living body. Medicina Philosophica,1992.11:620~629
93 H. D. Zeman, L. Gunnar. Enhancing the Contrast of Subcutaneous Vein. Proc, SPIE,1999, 3595: 219~230
94 H. D. Zeman, L. Gunnar, H. Deshmuk. Optimization of Subcutaneous Vein Contrast Enhancement. Proc.SPIE, 2000, 3911: 50~57
95 H. D. Zeman, L. Gunnar, C. Vrancken. Prototype Vein Contrast Enhancer. Proc, SPIE, 2004,5318(1):39~49
96 M. Kono, H. Ueki, S.I. Umemura, Near-infrared finger vein patterns for personal identification. Applied Optics, 2002. 41(35):7429~7436
97 S.K. Im, H.S.Choi, S.W.Kim, A Direction-Based Vascular Pattern Extraction Algorithm for Hand Vascular Pattern Verification ETRI Journal, 2000. 25(2):101~108
98 Im,S.K.,et al.,An Biometric Identification System by Extracting Hand Vein Patterns. Journal of the Korean Physical Society, 2001, 38(3): 268~272
99 Tanaka,T.and N.Kubo.Biometric authentication by hand vein patterns.in SICE 2004 Annual Conference.2004
100 J.M. Cross,C.L.Smith. Thermographic imaging of the subcutaneous vascular network of the back of the hand for verification of identity. In Proceedings. Institute of Electrical and Electronics Engineers 29th Annual 1995 International Carnahan Conference on Security Technology.1995
101 A.M. Badawi, Hand Vein Biometric Verification Prototype: A Testing Performance and Patterns Similarity, in Proceedings of The 2006 International Conference on Image Processing, Computer Vision, and Pattern Recognition. 2006: Las Vegas, USA.
102 L Wang, L. Graham, A thermal hand vein pattern verification system, in PatternRecognition and Image Analysis, S.Singh, et al., Editors. 2005, Springer Berlin/Heidelberg. 58~65
103 M Shahin, A Badawi, M Kamel. Biometric Authentication Using Fast Correlation of Near Infrared Hand Vein Patterns. International Journal of Biomedical Sciences, 2(3):141~148
104 A N Bashkatov, E A Genina, V I Kochubey, et al.Optical properties of human skin, subcutaneous and mucous tissues in the wavelength range from 400 to 2000nm, J. Phys.D:Appl.Phys,38(2006):2543~2555
105杜培胜,郭靖,董前民.液晶可调谐光片及其在光谱仪上的应用.红外,2007,28(11):4~8
106韩笑.人体手背静脉识别算法研究.吉林大学博士学位论文. 2007
107 A. Hoover, V. Kouznetsova, M. Goldbaum. Locating Blood Vessels in Retinal Images by Piecewise Threshold Probing of a Matched Filter Response. IEEE Trans. Med. Imag.. 2000,19(3):203~210
108 S. Chaudhuri, S. Chatterjee, N. Katz, et al. Detection of Blood Vessels in Retinal images Using Two-Dimensional Matched Filters. IEEE Trans. Med. Imag.. 1989,8(3):263~269
109 S. Tamura, K. Tanaka, S. Ohmori, K. Okazaki, A. Okada, M. Hoshi. Semiautomatic Leakage Analyzing System for Time Series Fluorescein Ocular Fundus Angiography. Pattern Recognition. 1983,16(2):149~162
110 Liang Zhou, Mark S. Rzeszotarski, Lawrence J. Singerman, et al. The Detection and Quantification of Retinopathy Using Digital Angiograms. IEEE Trans. Med. Imag.. 1994, 13(4): 619~625
111 Mark D.Fairchild. Color Appearance Models. Addison Wesley, Reading, Massachussets, 1998
112 Charles A.Poynton. Frequently Asked Questions about Color. http://www.inforamp.net/~poynton.
113 Hardie, R. C., Boncelet, C.. LUM Filters: A Class of Rank-Order-Based Filters for Smoothing and Sharpening. IEEE Transactions on Signal Processing. 1993,41(3):1061~1076
114 L. Vincent, P. Soille. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. IEEE Trans. Patt. Anal. Mach. Intell. 1991,13(6):583~598
115 J Bernsen. Dynamic thresholding of gray-level images. Proceedings of the 8thInternational Conference on Pattern Recognition. Paris: IEEE Computer Society Press, 1986
116李剑锋.面向中医诊断的自动舌图像处理系统.哈尔滨工业大学硕士论文. 2002
117郭振华.舌图像的颜色分析.哈尔滨工业大学硕士论文. 2004
118边肇祺,张学工.模式识别.清华大学. 2000,1(2):136~140,235~237
119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