乳腺X线影像的计算机辅助诊断新方法研究
摘要
乳腺癌是妇女常见的恶性肿瘤之一，早期发现、早期诊断、早期治疗是降
低乳腺癌死亡率的关键。乳腺钼靶 X 线摄影是目前临床诊断乳腺癌的有力工具。
但钼靶 X 线影像的信息只有很少部分能为人眼识别，即使富有经验的医生也很
难及时发现钼靶 X 线影像上早期乳腺癌的微小钙化点，以致延误病人的治疗时
机。可以说，实现乳癌早期诊断的关键技术之一是及时发现乳癌 X 影像中的微
小钙化并判断其是否有恶化倾向。随着计算机技术的飞速发展，基于传统乳腺
钼钯 X 线影像的计算机辅助检测微小钙化点已成为乳癌早期诊断的研究热点。
这主要是因为细小、颗粒状的成簇微钙化点是乳癌的一个重要早期表现。国外
统计资料表明占 30%～50%的乳腺恶性肿瘤伴有微钙化。因此，不断提高微小钙化
点的检出率和准确判别其恶性度成为众多学者孜孜以求的目标。
本文建立了一个基于模块化设计思路的计算机辅助诊断系统借以对乳腺钼
靶图像上的微钙化点进行检测和模式识别。该系统分为四个模块：①预处理模
块-对乳腺钼靶 X 片图像进行数字化和归一化处理，得到具有相同空间分辨率和
灰度分辨率的规格化图像，以便于计算机作进一步后续处理。②感兴趣区域(ROI)
提取模块-自动寻找并分割含有微钙化点的区域，以节省后续处理的工作量。本
研究将独立分量分析(ICA)用于乳腺 ROI 的特征提取，在此基础上用人工神经网
络(ANN)分类器进行模式识别。③微钙化点自动检测模块-实现乳腺 ROI 上微钙
化点的自动检测与定位。本文将差值图像去噪、阈值化分类技术和小波去噪、
ANN 分类技术分别用于乳腺 ROI，得到含高频信号和极高频噪声位置信息的二值
化图像及含高频信号和低频背景位置信息的二值化图像。将两者进行与操作得
到含微钙化点位置信息的二值化图像。④微钙化点病变类型识别模块-实现微钙
化点特征提取和优化及病变类型模式识别，给出初步诊断结果。本文建立了一
套表征微钙化点形态、纹理等特性的 33 维特征矢量。然后，用遗传算法进行特
征选择得到 17 维优化特征矢量，优化特征矢量与 ANN 组成判别模型完成微钙化
点病变类型的判定。上述各模块相互独立，可以单独改进和优化而不影响其它
模块。此外，本文将适用于小样本的支持矢量机(SVM)分类器应用于上述分类模
型中，并用接受者操作特征曲线(ROC)对分别由 ANN 和 SVM 分类器组成的判别模
型分类性能进行评价。
I
运用该系统对临床病例和标准乳腺库中乳腺图像进行分析，得到 87.5%(ANN)
和 90.0%(SVM)的乳腺 ROI 检出率、96.3%(ANN)和 97.0%(SVM)的微钙化点检出率
及 88.7%(ANN)和 93.0%(SVM)的恶性微钙化点识别率。结果证明了本文建立的计
算机辅助诊断系统具有较高的微钙化点检出率和较准确的恶性度判别性能，为
乳癌早诊研究提供了一套新方法。
本研究中的创新性主要体现在：①提出基于模块化的早期乳腺癌辅助诊断
系统设计思路，建立了基于微钙化点检测的早期乳腺癌计算机辅助诊断的完整
模型；②首次将独立分量分析成功地应用于乳腺图像 ROI 自动提取；③首次将
集合论思想应用到乳腺图像微钙化点的检测中，提出一种能发挥差值图像技术、
阈值化分类和小波变换技术、人工神经网络分类等多种技术优势的综合处理检
测方法；④建立了一套比较完整的可以表征乳腺图像微钙化点各类特异性的特
征参数矢量；⑤首次将现代统计学习理论引入到本研究中，并且成功地建立了
基于 SVM 的判别模型；⑥提出了基于 ROC 曲线的阈值选择方法和系统诊断价
值评价方法。
Breast cancer is one of the most common malignant diseases among women.
Clear evidence shows that early discovery, early diagnosis and early treatment of
breast cancer can significantly increase the chance of survival for patients.
Mammography is the most effective method for the early detection of breast cancer.
However normally, viewed mammograms display only a very small part of the total
information they contain. It is very hard to find the microcalcifications (MCCs) of
early breast cancer in mammograms even for an experienced radiologist. Therefore,
any increase in the detection and classification of MCCs will lead to further
improvement in its efficacy in the detection of early breast cancer. With the rapid
progress of computer technology, computer aided detection and identification of
MCCs have been a hot research field since clustered MCCs in mammograms are an
important sign for early detection of breast cancer. It is estimated that about 30% to
50% of breast carcinomas detected radiographically demonstrates MCCs in
mammograms. So the increase in the detection and classification of MCCs in
mammograms has been of interest to many researchers.
This paper presents a prototype of a computer-aided diagnostic system (CAD) for
mammography screening to automatically detect and classify MCCs in mammograms.
It comprises four modules. The first module, called the mammogram preprocessing
module, digitizes and normalizes the original mammogram, and makes it to be fit for
computer processing. Since the region of interest (ROI) covers only a small part of
the whole mammograms, the second module, called the ROI finder module, finds and
locates suspicious areas of MCCs. Independent component analysis is implemented to
extract the features of ROI, and artificial neural network(ANN) classifier is used to
label the region as either true or false ROI. Since only MCCs are of interest in
providing a sign of breast cancer, the third module, called the MCCs detection
module, is a computer automated MCCs detection system that takes as inputs the
ROIs provided by the ROI finder module. Two methods are used to detect the MCCs.
The first one based on difference-image technique is used to remove the
low-frequency background, while the second one based on wavelet denoising and
neural network classifying technique is used to remove the very high-frequency noise.
Signals coming out from two methods are combined through a logical AND operation
to get the final detected result that contains the position information of MCCs. Finally,
III
the fourth module, called the MCCs classification module, includes features
extraction, feature optimization and pattern recognition. A pool of many features (33)
with the information about shape, texture and so on of MCCs is computed. Genetic
algorithm is introduced to get the optimal features (17). The ANN classifier is used to
label the MCC as either malignant or benign. Moreover, support vector machine
relying on the statistical learning theory is applied to the patter recognition in the
research. One advantage of the designed system is that each module is a separate
component that can be individually upgraded to improve the whole system. Moreover,
receiver operation curve is used to evaluate the performance of each decision model
in this research. The above methods are adopted in the processing of the test samples
with a true positive rate (TPR) of 87.5% (ANN) and 90.0% (SVM) in the ROI
automated finder module, a TPR of 96.3% (ANN) and 97.0% (SVM) in the MCCs
detection module, a TPR of 88.7% (ANN) and 93.0% (SVM) in the MCCs
classification module. The SVM classifiers get slightly better results than the ANN
classifiers. The results show that the method has a high performance on the detection
and classification of MCCs, and gives a new method for the research on the diagnosis
of early breast cancer.
The originalities of this thesis are the followings:
1. One modularization design thought is int
低乳腺癌死亡率的关键。乳腺钼靶 X 线摄影是目前临床诊断乳腺癌的有力工具。
但钼靶 X 线影像的信息只有很少部分能为人眼识别，即使富有经验的医生也很
难及时发现钼靶 X 线影像上早期乳腺癌的微小钙化点，以致延误病人的治疗时
机。可以说，实现乳癌早期诊断的关键技术之一是及时发现乳癌 X 影像中的微
小钙化并判断其是否有恶化倾向。随着计算机技术的飞速发展，基于传统乳腺
钼钯 X 线影像的计算机辅助检测微小钙化点已成为乳癌早期诊断的研究热点。
这主要是因为细小、颗粒状的成簇微钙化点是乳癌的一个重要早期表现。国外
统计资料表明占 30%～50%的乳腺恶性肿瘤伴有微钙化。因此，不断提高微小钙化
点的检出率和准确判别其恶性度成为众多学者孜孜以求的目标。
本文建立了一个基于模块化设计思路的计算机辅助诊断系统借以对乳腺钼
靶图像上的微钙化点进行检测和模式识别。该系统分为四个模块：①预处理模
块-对乳腺钼靶 X 片图像进行数字化和归一化处理，得到具有相同空间分辨率和
灰度分辨率的规格化图像，以便于计算机作进一步后续处理。②感兴趣区域(ROI)
提取模块-自动寻找并分割含有微钙化点的区域，以节省后续处理的工作量。本
研究将独立分量分析(ICA)用于乳腺 ROI 的特征提取，在此基础上用人工神经网
络(ANN)分类器进行模式识别。③微钙化点自动检测模块-实现乳腺 ROI 上微钙
化点的自动检测与定位。本文将差值图像去噪、阈值化分类技术和小波去噪、
ANN 分类技术分别用于乳腺 ROI，得到含高频信号和极高频噪声位置信息的二值
化图像及含高频信号和低频背景位置信息的二值化图像。将两者进行与操作得
到含微钙化点位置信息的二值化图像。④微钙化点病变类型识别模块-实现微钙
化点特征提取和优化及病变类型模式识别，给出初步诊断结果。本文建立了一
套表征微钙化点形态、纹理等特性的 33 维特征矢量。然后，用遗传算法进行特
征选择得到 17 维优化特征矢量，优化特征矢量与 ANN 组成判别模型完成微钙化
点病变类型的判定。上述各模块相互独立，可以单独改进和优化而不影响其它
模块。此外，本文将适用于小样本的支持矢量机(SVM)分类器应用于上述分类模
型中，并用接受者操作特征曲线(ROC)对分别由 ANN 和 SVM 分类器组成的判别模
型分类性能进行评价。
I
运用该系统对临床病例和标准乳腺库中乳腺图像进行分析，得到 87.5%(ANN)
和 90.0%(SVM)的乳腺 ROI 检出率、96.3%(ANN)和 97.0%(SVM)的微钙化点检出率
及 88.7%(ANN)和 93.0%(SVM)的恶性微钙化点识别率。结果证明了本文建立的计
算机辅助诊断系统具有较高的微钙化点检出率和较准确的恶性度判别性能，为
乳癌早诊研究提供了一套新方法。
本研究中的创新性主要体现在：①提出基于模块化的早期乳腺癌辅助诊断
系统设计思路，建立了基于微钙化点检测的早期乳腺癌计算机辅助诊断的完整
模型；②首次将独立分量分析成功地应用于乳腺图像 ROI 自动提取；③首次将
集合论思想应用到乳腺图像微钙化点的检测中，提出一种能发挥差值图像技术、
阈值化分类和小波变换技术、人工神经网络分类等多种技术优势的综合处理检
测方法；④建立了一套比较完整的可以表征乳腺图像微钙化点各类特异性的特
征参数矢量；⑤首次将现代统计学习理论引入到本研究中，并且成功地建立了
基于 SVM 的判别模型；⑥提出了基于 ROC 曲线的阈值选择方法和系统诊断价
值评价方法。
Breast cancer is one of the most common malignant diseases among women.
Clear evidence shows that early discovery, early diagnosis and early treatment of
breast cancer can significantly increase the chance of survival for patients.
Mammography is the most effective method for the early detection of breast cancer.
However normally, viewed mammograms display only a very small part of the total
information they contain. It is very hard to find the microcalcifications (MCCs) of
early breast cancer in mammograms even for an experienced radiologist. Therefore,
any increase in the detection and classification of MCCs will lead to further
improvement in its efficacy in the detection of early breast cancer. With the rapid
progress of computer technology, computer aided detection and identification of
MCCs have been a hot research field since clustered MCCs in mammograms are an
important sign for early detection of breast cancer. It is estimated that about 30% to
50% of breast carcinomas detected radiographically demonstrates MCCs in
mammograms. So the increase in the detection and classification of MCCs in
mammograms has been of interest to many researchers.
This paper presents a prototype of a computer-aided diagnostic system (CAD) for
mammography screening to automatically detect and classify MCCs in mammograms.
It comprises four modules. The first module, called the mammogram preprocessing
module, digitizes and normalizes the original mammogram, and makes it to be fit for
computer processing. Since the region of interest (ROI) covers only a small part of
the whole mammograms, the second module, called the ROI finder module, finds and
locates suspicious areas of MCCs. Independent component analysis is implemented to
extract the features of ROI, and artificial neural network(ANN) classifier is used to
label the region as either true or false ROI. Since only MCCs are of interest in
providing a sign of breast cancer, the third module, called the MCCs detection
module, is a computer automated MCCs detection system that takes as inputs the
ROIs provided by the ROI finder module. Two methods are used to detect the MCCs.
The first one based on difference-image technique is used to remove the
low-frequency background, while the second one based on wavelet denoising and
neural network classifying technique is used to remove the very high-frequency noise.
Signals coming out from two methods are combined through a logical AND operation
to get the final detected result that contains the position information of MCCs. Finally,
III
the fourth module, called the MCCs classification module, includes features
extraction, feature optimization and pattern recognition. A pool of many features (33)
with the information about shape, texture and so on of MCCs is computed. Genetic
algorithm is introduced to get the optimal features (17). The ANN classifier is used to
label the MCC as either malignant or benign. Moreover, support vector machine
relying on the statistical learning theory is applied to the patter recognition in the
research. One advantage of the designed system is that each module is a separate
component that can be individually upgraded to improve the whole system. Moreover,
receiver operation curve is used to evaluate the performance of each decision model
in this research. The above methods are adopted in the processing of the test samples
with a true positive rate (TPR) of 87.5% (ANN) and 90.0% (SVM) in the ROI
automated finder module, a TPR of 96.3% (ANN) and 97.0% (SVM) in the MCCs
detection module, a TPR of 88.7% (ANN) and 93.0% (SVM) in the MCCs
classification module. The SVM classifiers get slightly better results than the ANN
classifiers. The results show that the method has a high performance on the detection
and classification of MCCs, and gives a new method for the research on the diagnosis
of early breast cancer.
The originalities of this thesis are the followings:
1. One modularization design thought is int
引文
参考文献
[1] 丁丽央，陈坤，沈高飞等，乳腺癌危险因素病例对照研究，中
国慢性病预防与控制，1998，6(6)：283－285
[2] Timothy Key, Pia K Verkaslo, Emily Banks, Epidemiology
of Breast Cancer, Lancet Oncology, 2001, 2:133-140
[3] Suketami Tominga, Tetsuo Kuroishi, Epidemiology of
Breast Cancer in Japan, Cancer Letters, 1995, 90:75-79
[4] 任丽梅，伸向女性的魔爪—乳腺癌，健康顾问，2001，2：34
[5] 汪毅，男性乳腺癌，重庆医学，2002，31（2）：146-148
[6] Ravandi-Kashani, F., Hayes, T.G., Male breast cancer: a
review of the literature, European Journal of Cancer Part
A,1998,34(9): 1341-1347
[7] 刘君，方志沂，乳腺癌的早期诊断，中国全科医学，2002，5
（6）：431－432
[8] 汤鹏，周卫平，早期乳腺癌的诊断体会，中国肿瘤临床与康复，
2002，9（1）：57-58
[9] 刘新杰，麦沛成，周冬仙，早期乳腺癌的诊断，现代诊断与治
疗，2001，12（6）：378-379
[10] Hulka, Barbara S., Moorman, Patricia G., Breast cancer:
hormones and other risk factors, Maturitas,2002,38(1):
103 - 113
[11] 方志沂，邱晓光，乳腺癌的分布规律及其危险因素，中国全科
医学，2002，5（6）：429－430
[12] 左文述，徐忠法主编，刘奇，现代乳腺肿瘤学，1996，济南：
山东科学技术出版社，51-63
[13] Fraser, Gary E., Shavlik, David, Risk Factors, Lifetime
Risk, and Age at Onset of Breast Cancer, Annals of
Epidemiology, 1997,7(6): 375-382
[14] 李佩文，邹丽琰主编，乳腺癌综合诊疗学，北京：中国中医药
出版社，1999，143-147
[15] 张军，巫向前，乳房癌实验室检测指标的进展，国外医学:临
床生物化学与检验分册，1994,15(4)：156-159
141
参考文献
[16] 高天欣，张永红，白净，乳腺癌检测新方法，医学影像，2002，
8（5）：46-48
[17] Getty, David J., Pickett, Ronald M., Stereoscopic digital
mammography: improving detection and diagnosis of breast
cancer, International Congress Series,2001,
1230:538-544
[18] 子荫，白木，早期乳腺癌诊疗新技术，抗癌，2002，（1）：37
[19] Teh, W., Wilson, A. R. M., The role of ultrasound in breast
cancer screening: A consensus statement by the European
Group for breast cancer screening, European Journal of
Cancer Part A,1998,34(4):449-450
[20] 石木兰，合理应用乳腺的影像学检查,提高乳腺病变的诊断质
量，中华放射学杂志，1999，33(2)：77-78
[21] 吴恩惠主编，中华影像医学-乳腺卷，北京：人民卫生出版社，
2002，3-12
[22] 王学成，乳腺癌的影像学诊断及评价，中外医用放射技术，
2000，(10)：4-5
[23] 蔡丰，张涛，乳腺癌的影像学诊断和治疗进展，中华超声影像
学杂志，2000,9(11)：704-705
[24] 顾素英，杨振东，乳腺癌的早期影像学诊断，蚌埠医学院学报，
2002，27(4)：373-374
[25] 谢则平，王晓芳，张先林，近红外光无创伤诊断乳腺癌的临床
研究，癌症，2000，2：185
[26] 张忠清，李广灿，叶召，乳腺癌当前流行趋势分析，中国肿瘤，
2000，9（10）：454-455
[27] Heng-Da Cheng, Yui Manli, Freimanis RI, A novel approach
to microcalcification detection using fuzzy logic
technique, IEEE Trans. On MI., 1998,17(3):442-450
[28] 李佩文，邹丽琰主编，乳腺癌综合诊疗学，北京：中国中医药
出版社，1999，179-180
[29] 华西医科大学主编，X 线诊断学，成都：四川科学技术出版社，
1987 年第二版
[30] 李树玲主编，乳腺肿瘤学，北京：科学技术出版社，2000：
169-175
142
参考文献
[31] 李佩文，邹丽琰主编，乳腺癌综合诊疗学，北京：中国中医药
出版社，1999，208-209
[32] 吴恩惠主编，中华影像医学-乳腺卷，北京：人民卫生出版社，
2002，116
[33] Wallis M, Walsh M, Lee J, A review of false positive
negative positive mammography in a symptomatic
population. Clinical Radiology, 1991,44:13-15
[34] Adler D, Helview M, Mammographic biopsy recommendations,
Current radiology, 1992,4:123-129
[35] Spiesberger W. Mammogram Inspection by Computer. IEEE
Trans. On BME,1979,26:213-219
[36] E.Kahn, A.Gavoille, J.Masselot, et al. Computer Analysis
of Breast Calcifications in Mammographic Images.
Comp-Assisted Radiology. 1987,729-733
[37] B.W.Fam, Olson SL, Winter PF,et al.Algorithm for the
Detection of fine Clustered Calcifications On film
mammograms. Radiol.1988,169(2):333-337
[38] D.H.Davies, D.R.Dance. Automatic Computer Detection of
Clustered Microcalcifications in Digital
Mammograms,Physics in Medicine and Biology.
1990,35(8):1111-1118
[39] H.P.Chan, Kunio Doi, Simranjit Galhotra, et al.
Improvement in Radiologists’ detection of Clustered
Microcalcifications on Mammograms. Med. Phys.
1990,25(10):1102-1110
[40] Mascio L,Hernandez M and Clinton L. Automated Analysis
for Microcalcifications in high Resolution Mammograms,
Proc. SPIE Vol. 1898, p. 472-479, Medical Imaging 1993:
Image Processing, Murray H. Loew, Ed.
[41] J.Dengler, S.Bechrens and J.F.Desaga. Segmentation of
Microcalcification in Mammograms. IEEE Trans. On MI,
1993,12(4):263-274
143
参考文献
[42] N. Karssemeijer, Recognition of Clustered
Microcalcifications Using A Random Field Model. SPIE
Proc.On Biomedical image processing and biomedical
visualization, 1993,1905:776-786
[43] N.Karssemeijer, Adaptive Noise Equalization and
Recognition of Microcalcification Clusters in Mammograms.
Init.J.Pattern Recognit. Artificial Intell. 1993,
7(6):1357-1376
[44] N.Karssemeijer.A Stochasic Model for Automated Detection
of Calcifications in Digital Mammograms. Image Vision
Comput. 1992,10(6):369-375
[45] L.Shen, R.M.Rangayyan. J.E.L.Desautels. Application of
Shape Analysis to Mammographic Calcifications. IEEE
Trans. On MI.1993,13(2):263-274
[46] Armando Bazzani, Alessandro Bevilacqua, Dante Bollini,
et al. Automatic Detection of Clustered
Microcalcifications in Digital Mammograms Using an SVM
Classifier. ESANN’2000 proceedings-European Symposium
on Artificial Neural Networks Bruges(Belgium),26-28
April 2000,D-Facto public, ISBN 2-930307-00-5: 195-200
[47] R.N.Strickland, H.I.Han.Wavelet Transform for Detecting
Microcalcifications in Mammograms.IEEE Trans. On
MI.1996,15(2):218-229
[48] Giuseppe Boccignone, Angelo Chianese and Antonio
Picariello. Computer Aided Detection of
Microcalcifications in Digital Mammograms. Computers in
Biology and Medicine, 2000,30:267-286
[49] Brijesh Verma and John Zakos. A Computer-Aided Diagnosis
System For Digital Mammograms Based On Fuzzy-Neural And
Feature Extraction Techniques.IEEE Trans. On Information
Technology in Biomedicine.2000
[50] Erich Sorantin, Ferdinand Schmidt, Heinz Mayer, et al.
Computer Aided Diagnosis of Clustered
Microcalcifications Using Artificial Neural Nets.
Journal of Computing and Information Technology - CIT 8,
2000, 2, 151-160
144
参考文献
[51] Amendolia S.R. Bisogni M.G. Bottigli U, et al. The CALMA
Project: A CAD Tool in Breast Radiography. Nuclear
instruments and Methods in Physics Research A,
4460(2001):107-112
[52] D.Brzakovic, M.Neskovic.Mammogram Screening Using
Multiresolution-Based Image Segmentation. International
Journal of Pattern Recognition and Artificial
Intelligence, 1993,7(6):1437-1460
[53] William Mark Morrow, Raman Bhalachandra Paranjape,
Rangaraj M.Rangayyan, et al, Region-Based Contrast
Enhancement of Mammograms, IEEE Trans. On MI.,1992,
1(3):392-406
[54] Songyang Yu and Ling Guan. A CAD System for the Automatic
Detection of Clustered Microc alcifications in Digitized
Mammogram Films. IEEE Trans. On MI,2000,19(2):115-126
[55] 马振鹤，乳腺 X 线片中微钙化点感兴趣区域提取方法的研究，
硕士学位论文，天津大学，2003-1-17
[56] 郭繁夏 编著，扫描仪的原理与开发应用，北京：清华大学出
版社，1996，5-22
[57] San-Kan Lee, Chien-Shun Lo, Chuin-Mu Wang, A
computer-aided design mammography screening system for
detection and classification of microcalcifications,
International Journal of Medical Informatics,
2000,60:29-57
[58] A. Papadopoulos, D.I. Fotiadis, A. Likas, An automatic
microcalcification detection system based on hybrid
neural network classifier, Artificial Intelligence in
Medicine, 2002, 25:149-167
[60] Stone, James V., Independent component analysis: an
introduction, Trends in Cognitive
Sciences,2002,6(2):59-64
[61] 杨福生，洪波，唐庆玉，独立分量分析及其在生物医学工程中
的应用，国外医学生物医学工程分册，2000，23（3）：129-134
[62] Yuen Pong C., Lai J.H., Face representation using
independent component analysis, 2002, 35(6): 1247-1257
145
参考文献
[63] Stone, James V., Independent component analysis: an
introduction, Trends in Cognitive
Sciences,2001,6(2):59-64
[64] Akaho, Shotaro, Conditionally independent component
analysis for supervised feature
extraction,Neurocomputing,2002,49(1):139-50
[65] Comon P. Independent component analysis, A new concept,
Signal Processing, 1994,36:287-314
[66] Hyv?rinen A., Oja, E., Independent component analysis:
algorithms and applications, Neural Networks, 2000,13(4):
411-430
[67] Hyv?rinen A., Fast and robust fixed-point algorithms for
independent component analysis, IEEE Trans. Neural
Networks, 1999，10(3)：626-634
[68] Bell AJ, An information maximization approach to blind
separation and blind deconvolution, Neural Computation,
1995, 7(6):1129-1159
[69] Lee TW, Independent component analysis using an extended
informax algorithm for mixed Subgaussian and
Supergaussian sources, Neural Computation, 1999,
11(2):409-433
[70] Theis, Fabian J.,Bauer, Ch.,Lang, Elmar W.,Comparison of
maximum entropy and minimal mutual information in a
nonlinear setting, Signal Processing,82(7):971-980
[71] 边肇祺，张学工，模式识别，北京：清华大学出版社，2000，
250-271
[72] 章毓晋，图像分割，北京：科学出版社，2001，85-87
[73] Son Hye-Kyung, Yun Mijin, Jeon Tae Joo, et al., ROC
analysis of ordered subset expectation maximization and
filtered back projection technique for FDG-PET in lung
cancer, IEEE Transactions on Nuclear Science, 2003, 50(1):
37-41
146
参考文献
[74] Bowyer Kevin, Kranenburg Christine, Dougherty, Sean,
Edge Detector Evaluation Using Empirical ROC Curves,
Computer Vision and Image
Understanding,2001,84(1):77-103
[75] Ownby, R.L., ROC curve analyses of neuropsychological
tests in Alzheimer's disease, Archives of Clinical
Neuropsychology,,2000,15(8):748
[76] Lind, Pehr A., Marks, Lawrence B., Hollis, Donna,et al.,
Receiver operating characteristic curves to assess
predictors of radiation-induced symptomatic lung injury,
International Journal of Radiation Oncology Biology
Physics,2002,54(2):340-347
[77] Ferreira Rocha, Analysis of mammogram classification
using a wavelet transform decomposition, Pattern
Recognition Letters,2003, 24(7):973-982
[78] Sentelle S., Multiresolution-Based Segmentation of
Calcifications for the Early Detection of Breast Cancer,
Real-Time Imaging, 2002, 8(3):237-252
[80] Zhang L., Sankar R., Qian W., Advances in
micro-calcification clusters detection in mammography,
Computers in Biology and Medicine,2002,32(6):515-528
[81] Gulsrud T.O., Husoy J.H., Optimal filter-based detection
of microcalcifications, IEEE Transactions on Biomedical
Engineering, 2001,48(11):1272-1281
[82] Campos Raul Mata, Vidal Eva Maria, Nava Enrique,
Detection of microcalcifications by means of multiscale
methods and statistical techniques, Journal of Digital
Imaging,2000,13(1):221-225
[83] 杨福生，小波变换的工程分析与应用，北京：科学出版社，1999，
42-139
[84] Mallat. S. G., A theory for multi-resolution signal
decomposition: the wavelet representation. IEEE. Trans.
Pattn. Anal. Mach Intell, 1989, 11(7):674-693
147
参考文献
[85] Nishikawa R, Gier M, Doi K, et al. Computer-aided
detection and diagnosis of masses and clustered
microcalcifications from digital mammograms. In state
in the art of digital mammographic image analysis, London:
World scientific Publishing, 1994: 82-102
[86] Brijesh Verma, A neural network based techniques to
locate and classify microcalcifications in digital
mammograms. Proceedings of IEEE World Congress on
Computational Intelligence, WCCI'98, USA.1998:
2163-2168
[87] Ren Mingwu, Yang Jingyu, Sun Han, Tracing boundary
contours in a binary image, Image and Vision
Computing,2002, 20(2):125-131
[88] Chim Y. C., Kassim A. A., Ibrahim Y., Character
recognition using statistical moments, Image and Vision
Computing, 1999, 17(3): 299-307
[89] Rajasekaran S., Pai G. A., Image recognition using
simplified fuzzy art map augmented with a moment based
feature extractor, International Journal of Pattern
Recognition and Artificial Intelligence,
2000,14(8):1081-1095
[90] Stern Adrian, Kruchakov Inna, Yoavi Eitan, et al,
Recognition of motion-blurred images by use of the method
of moments, Applied Optics,2002,41(11):2164-2171
[91] Bowman E.T.,Soga K.,Drummond W., Particle shape
characterisation using Fourier descriptor
analysis,Geotechnique,2001，51(6):545-554
[92] Neumann Richard, Teisseron Gilbert, Extraction of
dominant points by estimation of the contour
fluctuations,Pattern Recognition,2002,35(7):1447-1462
[93] Boris Kovalerchuk, Evangelos Triantaphyllou, James F.
Ruiz, et al., The reliability issue of computer aided
breast cancer diagnosis, Computers and Biomedical
Research, 2000, 33:296-313
148
参考文献
[94] Kerr L.,Yoshiassu R.M., Palmeira M.A.M., Breast area of
disrupted architecture and texture, an indirect sign of
malignancy, Ultrasound in Medicine and
Biology,1997,23(1):8615
[95] Kwak Nojun, Choi Chong-Ho, Input feature selection by
mutual information based on Parzen window, IEEE
Transactions on Pattern Analysis and Machine
Intelligence, 2002, 24(12): 1667-1671
[96] 边肇祺，张学工，模式识别，北京：清华大学出版社，2000，
178-179
[97] 王凌，智能优化算法及其应用，北京：清华大学出版社，2001，
1-14
[98] Goldberg D. E., Genetic algorithms in search,
optimization and machine learning, New York: Addison
Weseley, 1989
[99] 张学工.关于统计学习理论与支持矢量机[J].自 动化学报,
2000, 26(1): 32-42
[100] Christopher J.C. Burges. A Tutorial on Support Vector
Machines for Pattern Recognition [J]. Data Mining and
Knowledge Discovery , 1998, 2:121-167
[101] Kononenko I., Machine learning for medical diagnosis:
History, state of the art and perspective,Artificial
Intelligence in Medicine, 2001,23(1):89-109
[102] 范劲松，SVM 理论及其应用的研究，博士，中国科学技术大学，
2000
[103] V. Vapnic, An overview of statistical learning theory,
IEEE Trans. On neural network, 1999, 10(5): 988-999
[104] Evgeniou Theodoros, Pontil Massimiliano, Poggio Tomaso,
Statistical Learning Theory: A primer, International
Journal of Computer Vision,2000, 38(1): 9-13
[105] 张学工，统计学习理论的本质，北京：清华大学出版社，2000
149
参考文献
[106] Cherkassky V., Mulier F., Vapnik-Chervonenkis (VC)
learning theory and its applications, IEEE Transactions
on Neural Networks,1999,10(5):985-987
[107] Anguita Davide, Boni Andrea, Ridella Sandro, Evaluating
the generalization ability of support vector machines
through the bootstrap, Neural Processing Letters,
2000,11(1):51-58
[108] Osuna E., Freund R., Girosi F., An improved training
algorithm for support vector machines, Neural Networks
for Signal Processing [1997] VII. Proceedings of the 1997
IEEE Workshop, 24-26,Sep 1997, 276 –285
[109] Brown Martin, Gunn Steve R., Lewis Hugh G,Support vector
machines for optimal classification and spectral
unmixing, Ecological Modelling,1999, 120(2), 167 - 179
[110] Burbidge R., Trotter M., Buxton B.,et al.,Drug design by
machine learning: support vector machines for
pharmaceutical data analysis, Computers and Chemistry,
2001, 26(1):5-14
150
发表学术论文情况
发表学术论文情况
期刊
1．王瑞平，万柏坤，SVM 算法及其在乳腺 X 片微钙化点自动检测中
的应用，电子学报。（2002，投稿，在审）
2．Baikun Wan, Ruiping Wang, Attracting the Cancer Information on
Mammograms by Using an Intelligent Computer-aided Detection
Algorithm, The Journal of Three Dimensional Images, 2003, 17(1):
155-160
3．王瑞平，万柏坤，基于综合处理方法的乳腺 X 影像中微钙化点检测
新技术，中国生物医学工程杂志，2002，21（6）：536-542
4．马振鹤，万柏坤，王瑞平，冠状动脉造影图像降噪处理的三种方法
比较，生物医学工程与临床，2002，6（3）：129-131
5．马振鹤，万柏坤，王瑞平，瘢痕色度分析法用于评估烧伤疗效的研
究，医疗卫生装备，2002，23（6）：7-10
6．王瑞平，万柏坤，乳腺癌早期诊断的计算机处理研究，天津大学学
报，2002， 35（4）：497-500
7．王瑞平，万柏坤，乳腺钼钯 X 射线影像中微钙化点的检测方法，国
外医学生物医学工程分册，2001，24（5）：212-217
8．王瑞平，李迎新，赵秋生，第二代激光光动力学肿瘤治疗系统研制，
中国医学物理学杂志，2000，17（3）：146-147
9．高卫平，王瑞平，PDT 半导体激光治疗机电源及控制系统的研制，
天津医科大学学报，1999，5（2）：9-10
10． 王瑞平，光动力疗法在肿瘤治疗中的应用，国外医学生物医学工程
分册，1999，22（6）：355-359
会议论文
1．Wang Ruiping, Wan Baikun, Cao Xuchen, Detecting Microcalcifications in
Mammogram Based on SVM Method, The First International Conference on
Machine Learning and Cybernetics, Beijing, China, November, 2002.
2．Wang Ruiping, Wan Baikun, Automated detection for MCCs in digital
151
发表学术论文情况
mammograms using difference-image technique, Proceedings of SPIE,
Electronic Imaging and Multimedia Technology III, Shanghai, China,
October, 2002,110-113(ISTP 检索，IDS Number: BV65D)
3．Wang Ruiping, Wan Baikun, Computer aided detection of
microcalcifications in digital mammograms using a WT technique,
Proceedings of SPIE, Electronic Imaging and Multimedia Technology
III, Shanghai, China, October, 2002, 530-533( ISTP 检索，IDS Number:
BV65D).
4．Baikun Wan, Ruiping Wang, Attracting the Cancer Information on
Mammograms by Using an Intelligent Computer-Aided Detection
Algorithm, CIT2002 Proceedings, Aizu-Wakamatsu, Japan, September,
2002, 131-133.
5．Baikun Wan, Ruiping Wang, Computer Aided Detection of
Micro-calcifications in Digital Mammograms by Using an ANN
Classifier, CIT2002 Proceedings, Aizu-Wakamatsu, Japan, September,
2002, 175-177.
6．Wang Ruiping, Wan Baikun, Cao Xuchen, Computer aided detection of
microcalcifications in digital mammograms using a synthetic
technique, Proceedings of International Conference on Image and
Graphics, Hefei, China, August, 2002, 639-644.（EI 检索，IDS Number:
02527290148；ISTP 检索，IDS Number:BV62P）
7．王瑞平，万柏坤，乳癌计算机辅助诊断系统的研究，天津生物医学
工程 2001 年会，天津, 2002 年 5 月，25-26
8．孙滨建，万柏坤，王瑞平，乳腺钼靶 X 片中微钙化点纹理特征提取
的研究，天津生物医学工程 2001 年会，天津, 2002 年 5 月，40
9．李树楠，万柏坤，王瑞平，乳腺钼靶 X 片微钙化点圆形度算法的研
究，天津生物医学工程 2001 年会，天津, 2002 年 5 月，41
10． 万柏坤，王瑞平，选择性冠状动脉造影图像边缘提取算法的研究，
天津市图像图形学学会 2001 年年会，2001 年 10 月，106-109
11． 王瑞平，万柏坤，乳腺 X 片微钙化点的自动检测技术，天津生物医
学工程 2000 年会，天津, 2001，12-13
[1] 丁丽央，陈坤，沈高飞等，乳腺癌危险因素病例对照研究，中
国慢性病预防与控制，1998，6(6)：283－285
[2] Timothy Key, Pia K Verkaslo, Emily Banks, Epidemiology
of Breast Cancer, Lancet Oncology, 2001, 2:133-140
[3] Suketami Tominga, Tetsuo Kuroishi, Epidemiology of
Breast Cancer in Japan, Cancer Letters, 1995, 90:75-79
[4] 任丽梅，伸向女性的魔爪—乳腺癌，健康顾问，2001，2：34
[5] 汪毅，男性乳腺癌，重庆医学，2002，31（2）：146-148
[6] Ravandi-Kashani, F., Hayes, T.G., Male breast cancer: a
review of the literature, European Journal of Cancer Part
A,1998,34(9): 1341-1347
[7] 刘君，方志沂，乳腺癌的早期诊断，中国全科医学，2002，5
（6）：431－432
[8] 汤鹏，周卫平，早期乳腺癌的诊断体会，中国肿瘤临床与康复，
2002，9（1）：57-58
[9] 刘新杰，麦沛成，周冬仙，早期乳腺癌的诊断，现代诊断与治
疗，2001，12（6）：378-379
[10] Hulka, Barbara S., Moorman, Patricia G., Breast cancer:
hormones and other risk factors, Maturitas,2002,38(1):
103 - 113
[11] 方志沂，邱晓光，乳腺癌的分布规律及其危险因素，中国全科
医学，2002，5（6）：429－430
[12] 左文述，徐忠法主编，刘奇，现代乳腺肿瘤学，1996，济南：
山东科学技术出版社，51-63
[13] Fraser, Gary E., Shavlik, David, Risk Factors, Lifetime
Risk, and Age at Onset of Breast Cancer, Annals of
Epidemiology, 1997,7(6): 375-382
[14] 李佩文，邹丽琰主编，乳腺癌综合诊疗学，北京：中国中医药
出版社，1999，143-147
[15] 张军，巫向前，乳房癌实验室检测指标的进展，国外医学:临
床生物化学与检验分册，1994,15(4)：156-159
141
参考文献
[16] 高天欣，张永红，白净，乳腺癌检测新方法，医学影像，2002，
8（5）：46-48
[17] Getty, David J., Pickett, Ronald M., Stereoscopic digital
mammography: improving detection and diagnosis of breast
cancer, International Congress Series,2001,
1230:538-544
[18] 子荫，白木，早期乳腺癌诊疗新技术，抗癌，2002，（1）：37
[19] Teh, W., Wilson, A. R. M., The role of ultrasound in breast
cancer screening: A consensus statement by the European
Group for breast cancer screening, European Journal of
Cancer Part A,1998,34(4):449-450
[20] 石木兰，合理应用乳腺的影像学检查,提高乳腺病变的诊断质
量，中华放射学杂志，1999，33(2)：77-78
[21] 吴恩惠主编，中华影像医学-乳腺卷，北京：人民卫生出版社，
2002，3-12
[22] 王学成，乳腺癌的影像学诊断及评价，中外医用放射技术，
2000，(10)：4-5
[23] 蔡丰，张涛，乳腺癌的影像学诊断和治疗进展，中华超声影像
学杂志，2000,9(11)：704-705
[24] 顾素英，杨振东，乳腺癌的早期影像学诊断，蚌埠医学院学报，
2002，27(4)：373-374
[25] 谢则平，王晓芳，张先林，近红外光无创伤诊断乳腺癌的临床
研究，癌症，2000，2：185
[26] 张忠清，李广灿，叶召，乳腺癌当前流行趋势分析，中国肿瘤，
2000，9（10）：454-455
[27] Heng-Da Cheng, Yui Manli, Freimanis RI, A novel approach
to microcalcification detection using fuzzy logic
technique, IEEE Trans. On MI., 1998,17(3):442-450
[28] 李佩文，邹丽琰主编，乳腺癌综合诊疗学，北京：中国中医药
出版社，1999，179-180
[29] 华西医科大学主编，X 线诊断学，成都：四川科学技术出版社，
1987 年第二版
[30] 李树玲主编，乳腺肿瘤学，北京：科学技术出版社，2000：
169-175
142
参考文献
[31] 李佩文，邹丽琰主编，乳腺癌综合诊疗学，北京：中国中医药
出版社，1999，208-209
[32] 吴恩惠主编，中华影像医学-乳腺卷，北京：人民卫生出版社，
2002，116
[33] Wallis M, Walsh M, Lee J, A review of false positive
negative positive mammography in a symptomatic
population. Clinical Radiology, 1991,44:13-15
[34] Adler D, Helview M, Mammographic biopsy recommendations,
Current radiology, 1992,4:123-129
[35] Spiesberger W. Mammogram Inspection by Computer. IEEE
Trans. On BME,1979,26:213-219
[36] E.Kahn, A.Gavoille, J.Masselot, et al. Computer Analysis
of Breast Calcifications in Mammographic Images.
Comp-Assisted Radiology. 1987,729-733
[37] B.W.Fam, Olson SL, Winter PF,et al.Algorithm for the
Detection of fine Clustered Calcifications On film
mammograms. Radiol.1988,169(2):333-337
[38] D.H.Davies, D.R.Dance. Automatic Computer Detection of
Clustered Microcalcifications in Digital
Mammograms,Physics in Medicine and Biology.
1990,35(8):1111-1118
[39] H.P.Chan, Kunio Doi, Simranjit Galhotra, et al.
Improvement in Radiologists’ detection of Clustered
Microcalcifications on Mammograms. Med. Phys.
1990,25(10):1102-1110
[40] Mascio L,Hernandez M and Clinton L. Automated Analysis
for Microcalcifications in high Resolution Mammograms,
Proc. SPIE Vol. 1898, p. 472-479, Medical Imaging 1993:
Image Processing, Murray H. Loew, Ed.
[41] J.Dengler, S.Bechrens and J.F.Desaga. Segmentation of
Microcalcification in Mammograms. IEEE Trans. On MI,
1993,12(4):263-274
143
参考文献
[42] N. Karssemeijer, Recognition of Clustered
Microcalcifications Using A Random Field Model. SPIE
Proc.On Biomedical image processing and biomedical
visualization, 1993,1905:776-786
[43] N.Karssemeijer, Adaptive Noise Equalization and
Recognition of Microcalcification Clusters in Mammograms.
Init.J.Pattern Recognit. Artificial Intell. 1993,
7(6):1357-1376
[44] N.Karssemeijer.A Stochasic Model for Automated Detection
of Calcifications in Digital Mammograms. Image Vision
Comput. 1992,10(6):369-375
[45] L.Shen, R.M.Rangayyan. J.E.L.Desautels. Application of
Shape Analysis to Mammographic Calcifications. IEEE
Trans. On MI.1993,13(2):263-274
[46] Armando Bazzani, Alessandro Bevilacqua, Dante Bollini,
et al. Automatic Detection of Clustered
Microcalcifications in Digital Mammograms Using an SVM
Classifier. ESANN’2000 proceedings-European Symposium
on Artificial Neural Networks Bruges(Belgium),26-28
April 2000,D-Facto public, ISBN 2-930307-00-5: 195-200
[47] R.N.Strickland, H.I.Han.Wavelet Transform for Detecting
Microcalcifications in Mammograms.IEEE Trans. On
MI.1996,15(2):218-229
[48] Giuseppe Boccignone, Angelo Chianese and Antonio
Picariello. Computer Aided Detection of
Microcalcifications in Digital Mammograms. Computers in
Biology and Medicine, 2000,30:267-286
[49] Brijesh Verma and John Zakos. A Computer-Aided Diagnosis
System For Digital Mammograms Based On Fuzzy-Neural And
Feature Extraction Techniques.IEEE Trans. On Information
Technology in Biomedicine.2000
[50] Erich Sorantin, Ferdinand Schmidt, Heinz Mayer, et al.
Computer Aided Diagnosis of Clustered
Microcalcifications Using Artificial Neural Nets.
Journal of Computing and Information Technology - CIT 8,
2000, 2, 151-160
144
参考文献
[51] Amendolia S.R. Bisogni M.G. Bottigli U, et al. The CALMA
Project: A CAD Tool in Breast Radiography. Nuclear
instruments and Methods in Physics Research A,
4460(2001):107-112
[52] D.Brzakovic, M.Neskovic.Mammogram Screening Using
Multiresolution-Based Image Segmentation. International
Journal of Pattern Recognition and Artificial
Intelligence, 1993,7(6):1437-1460
[53] William Mark Morrow, Raman Bhalachandra Paranjape,
Rangaraj M.Rangayyan, et al, Region-Based Contrast
Enhancement of Mammograms, IEEE Trans. On MI.,1992,
1(3):392-406
[54] Songyang Yu and Ling Guan. A CAD System for the Automatic
Detection of Clustered Microc alcifications in Digitized
Mammogram Films. IEEE Trans. On MI,2000,19(2):115-126
[55] 马振鹤，乳腺 X 线片中微钙化点感兴趣区域提取方法的研究，
硕士学位论文，天津大学，2003-1-17
[56] 郭繁夏 编著，扫描仪的原理与开发应用，北京：清华大学出
版社，1996，5-22
[57] San-Kan Lee, Chien-Shun Lo, Chuin-Mu Wang, A
computer-aided design mammography screening system for
detection and classification of microcalcifications,
International Journal of Medical Informatics,
2000,60:29-57
[58] A. Papadopoulos, D.I. Fotiadis, A. Likas, An automatic
microcalcification detection system based on hybrid
neural network classifier, Artificial Intelligence in
Medicine, 2002, 25:149-167
[60] Stone, James V., Independent component analysis: an
introduction, Trends in Cognitive
Sciences,2002,6(2):59-64
[61] 杨福生，洪波，唐庆玉，独立分量分析及其在生物医学工程中
的应用，国外医学生物医学工程分册，2000，23（3）：129-134
[62] Yuen Pong C., Lai J.H., Face representation using
independent component analysis, 2002, 35(6): 1247-1257
145
参考文献
[63] Stone, James V., Independent component analysis: an
introduction, Trends in Cognitive
Sciences,2001,6(2):59-64
[64] Akaho, Shotaro, Conditionally independent component
analysis for supervised feature
extraction,Neurocomputing,2002,49(1):139-50
[65] Comon P. Independent component analysis, A new concept,
Signal Processing, 1994,36:287-314
[66] Hyv?rinen A., Oja, E., Independent component analysis:
algorithms and applications, Neural Networks, 2000,13(4):
411-430
[67] Hyv?rinen A., Fast and robust fixed-point algorithms for
independent component analysis, IEEE Trans. Neural
Networks, 1999，10(3)：626-634
[68] Bell AJ, An information maximization approach to blind
separation and blind deconvolution, Neural Computation,
1995, 7(6):1129-1159
[69] Lee TW, Independent component analysis using an extended
informax algorithm for mixed Subgaussian and
Supergaussian sources, Neural Computation, 1999,
11(2):409-433
[70] Theis, Fabian J.,Bauer, Ch.,Lang, Elmar W.,Comparison of
maximum entropy and minimal mutual information in a
nonlinear setting, Signal Processing,82(7):971-980
[71] 边肇祺，张学工，模式识别，北京：清华大学出版社，2000，
250-271
[72] 章毓晋，图像分割，北京：科学出版社，2001，85-87
[73] Son Hye-Kyung, Yun Mijin, Jeon Tae Joo, et al., ROC
analysis of ordered subset expectation maximization and
filtered back projection technique for FDG-PET in lung
cancer, IEEE Transactions on Nuclear Science, 2003, 50(1):
37-41
146
参考文献
[74] Bowyer Kevin, Kranenburg Christine, Dougherty, Sean,
Edge Detector Evaluation Using Empirical ROC Curves,
Computer Vision and Image
Understanding,2001,84(1):77-103
[75] Ownby, R.L., ROC curve analyses of neuropsychological
tests in Alzheimer's disease, Archives of Clinical
Neuropsychology,,2000,15(8):748
[76] Lind, Pehr A., Marks, Lawrence B., Hollis, Donna,et al.,
Receiver operating characteristic curves to assess
predictors of radiation-induced symptomatic lung injury,
International Journal of Radiation Oncology Biology
Physics,2002,54(2):340-347
[77] Ferreira Rocha, Analysis of mammogram classification
using a wavelet transform decomposition, Pattern
Recognition Letters,2003, 24(7):973-982
[78] Sentelle S., Multiresolution-Based Segmentation of
Calcifications for the Early Detection of Breast Cancer,
Real-Time Imaging, 2002, 8(3):237-252
[80] Zhang L., Sankar R., Qian W., Advances in
micro-calcification clusters detection in mammography,
Computers in Biology and Medicine,2002,32(6):515-528
[81] Gulsrud T.O., Husoy J.H., Optimal filter-based detection
of microcalcifications, IEEE Transactions on Biomedical
Engineering, 2001,48(11):1272-1281
[82] Campos Raul Mata, Vidal Eva Maria, Nava Enrique,
Detection of microcalcifications by means of multiscale
methods and statistical techniques, Journal of Digital
Imaging,2000,13(1):221-225
[83] 杨福生，小波变换的工程分析与应用，北京：科学出版社，1999，
42-139
[84] Mallat. S. G., A theory for multi-resolution signal
decomposition: the wavelet representation. IEEE. Trans.
Pattn. Anal. Mach Intell, 1989, 11(7):674-693
147
参考文献
[85] Nishikawa R, Gier M, Doi K, et al. Computer-aided
detection and diagnosis of masses and clustered
microcalcifications from digital mammograms. In state
in the art of digital mammographic image analysis, London:
World scientific Publishing, 1994: 82-102
[86] Brijesh Verma, A neural network based techniques to
locate and classify microcalcifications in digital
mammograms. Proceedings of IEEE World Congress on
Computational Intelligence, WCCI'98, USA.1998:
2163-2168
[87] Ren Mingwu, Yang Jingyu, Sun Han, Tracing boundary
contours in a binary image, Image and Vision
Computing,2002, 20(2):125-131
[88] Chim Y. C., Kassim A. A., Ibrahim Y., Character
recognition using statistical moments, Image and Vision
Computing, 1999, 17(3): 299-307
[89] Rajasekaran S., Pai G. A., Image recognition using
simplified fuzzy art map augmented with a moment based
feature extractor, International Journal of Pattern
Recognition and Artificial Intelligence,
2000,14(8):1081-1095
[90] Stern Adrian, Kruchakov Inna, Yoavi Eitan, et al,
Recognition of motion-blurred images by use of the method
of moments, Applied Optics,2002,41(11):2164-2171
[91] Bowman E.T.,Soga K.,Drummond W., Particle shape
characterisation using Fourier descriptor
analysis,Geotechnique,2001，51(6):545-554
[92] Neumann Richard, Teisseron Gilbert, Extraction of
dominant points by estimation of the contour
fluctuations,Pattern Recognition,2002,35(7):1447-1462
[93] Boris Kovalerchuk, Evangelos Triantaphyllou, James F.
Ruiz, et al., The reliability issue of computer aided
breast cancer diagnosis, Computers and Biomedical
Research, 2000, 33:296-313
148
参考文献
[94] Kerr L.,Yoshiassu R.M., Palmeira M.A.M., Breast area of
disrupted architecture and texture, an indirect sign of
malignancy, Ultrasound in Medicine and
Biology,1997,23(1):8615
[95] Kwak Nojun, Choi Chong-Ho, Input feature selection by
mutual information based on Parzen window, IEEE
Transactions on Pattern Analysis and Machine
Intelligence, 2002, 24(12): 1667-1671
[96] 边肇祺，张学工，模式识别，北京：清华大学出版社，2000，
178-179
[97] 王凌，智能优化算法及其应用，北京：清华大学出版社，2001，
1-14
[98] Goldberg D. E., Genetic algorithms in search,
optimization and machine learning, New York: Addison
Weseley, 1989
[99] 张学工.关于统计学习理论与支持矢量机[J].自 动化学报,
2000, 26(1): 32-42
[100] Christopher J.C. Burges. A Tutorial on Support Vector
Machines for Pattern Recognition [J]. Data Mining and
Knowledge Discovery , 1998, 2:121-167
[101] Kononenko I., Machine learning for medical diagnosis:
History, state of the art and perspective,Artificial
Intelligence in Medicine, 2001,23(1):89-109
[102] 范劲松，SVM 理论及其应用的研究，博士，中国科学技术大学，
2000
[103] V. Vapnic, An overview of statistical learning theory,
IEEE Trans. On neural network, 1999, 10(5): 988-999
[104] Evgeniou Theodoros, Pontil Massimiliano, Poggio Tomaso,
Statistical Learning Theory: A primer, International
Journal of Computer Vision,2000, 38(1): 9-13
[105] 张学工，统计学习理论的本质，北京：清华大学出版社，2000
149
参考文献
[106] Cherkassky V., Mulier F., Vapnik-Chervonenkis (VC)
learning theory and its applications, IEEE Transactions
on Neural Networks,1999,10(5):985-987
[107] Anguita Davide, Boni Andrea, Ridella Sandro, Evaluating
the generalization ability of support vector machines
through the bootstrap, Neural Processing Letters,
2000,11(1):51-58
[108] Osuna E., Freund R., Girosi F., An improved training
algorithm for support vector machines, Neural Networks
for Signal Processing [1997] VII. Proceedings of the 1997
IEEE Workshop, 24-26,Sep 1997, 276 –285
[109] Brown Martin, Gunn Steve R., Lewis Hugh G,Support vector
machines for optimal classification and spectral
unmixing, Ecological Modelling,1999, 120(2), 167 - 179
[110] Burbidge R., Trotter M., Buxton B.,et al.,Drug design by
machine learning: support vector machines for
pharmaceutical data analysis, Computers and Chemistry,
2001, 26(1):5-14
150
发表学术论文情况
发表学术论文情况
期刊
1．王瑞平，万柏坤，SVM 算法及其在乳腺 X 片微钙化点自动检测中
的应用，电子学报。（2002，投稿，在审）
2．Baikun Wan, Ruiping Wang, Attracting the Cancer Information on
Mammograms by Using an Intelligent Computer-aided Detection
Algorithm, The Journal of Three Dimensional Images, 2003, 17(1):
155-160
3．王瑞平，万柏坤，基于综合处理方法的乳腺 X 影像中微钙化点检测
新技术，中国生物医学工程杂志，2002，21（6）：536-542
4．马振鹤，万柏坤，王瑞平，冠状动脉造影图像降噪处理的三种方法
比较，生物医学工程与临床，2002，6（3）：129-131
5．马振鹤，万柏坤，王瑞平，瘢痕色度分析法用于评估烧伤疗效的研
究，医疗卫生装备，2002，23（6）：7-10
6．王瑞平，万柏坤，乳腺癌早期诊断的计算机处理研究，天津大学学
报，2002， 35（4）：497-500
7．王瑞平，万柏坤，乳腺钼钯 X 射线影像中微钙化点的检测方法，国
外医学生物医学工程分册，2001，24（5）：212-217
8．王瑞平，李迎新，赵秋生，第二代激光光动力学肿瘤治疗系统研制，
中国医学物理学杂志，2000，17（3）：146-147
9．高卫平，王瑞平，PDT 半导体激光治疗机电源及控制系统的研制，
天津医科大学学报，1999，5（2）：9-10
10． 王瑞平，光动力疗法在肿瘤治疗中的应用，国外医学生物医学工程
分册，1999，22（6）：355-359
会议论文
1．Wang Ruiping, Wan Baikun, Cao Xuchen, Detecting Microcalcifications in
Mammogram Based on SVM Method, The First International Conference on
Machine Learning and Cybernetics, Beijing, China, November, 2002.
2．Wang Ruiping, Wan Baikun, Automated detection for MCCs in digital
151
发表学术论文情况
mammograms using difference-image technique, Proceedings of SPIE,
Electronic Imaging and Multimedia Technology III, Shanghai, China,
October, 2002,110-113(ISTP 检索，IDS Number: BV65D)
3．Wang Ruiping, Wan Baikun, Computer aided detection of
microcalcifications in digital mammograms using a WT technique,
Proceedings of SPIE, Electronic Imaging and Multimedia Technology
III, Shanghai, China, October, 2002, 530-533( ISTP 检索，IDS Number:
BV65D).
4．Baikun Wan, Ruiping Wang, Attracting the Cancer Information on
Mammograms by Using an Intelligent Computer-Aided Detection
Algorithm, CIT2002 Proceedings, Aizu-Wakamatsu, Japan, September,
2002, 131-133.
5．Baikun Wan, Ruiping Wang, Computer Aided Detection of
Micro-calcifications in Digital Mammograms by Using an ANN
Classifier, CIT2002 Proceedings, Aizu-Wakamatsu, Japan, September,
2002, 175-177.
6．Wang Ruiping, Wan Baikun, Cao Xuchen, Computer aided detection of
microcalcifications in digital mammograms using a synthetic
technique, Proceedings of International Conference on Image and
Graphics, Hefei, China, August, 2002, 639-644.（EI 检索，IDS Number:
02527290148；ISTP 检索，IDS Number:BV62P）
7．王瑞平，万柏坤，乳癌计算机辅助诊断系统的研究，天津生物医学
工程 2001 年会，天津, 2002 年 5 月，25-26
8．孙滨建，万柏坤，王瑞平，乳腺钼靶 X 片中微钙化点纹理特征提取
的研究，天津生物医学工程 2001 年会，天津, 2002 年 5 月，40
9．李树楠，万柏坤，王瑞平，乳腺钼靶 X 片微钙化点圆形度算法的研
究，天津生物医学工程 2001 年会，天津, 2002 年 5 月，41
10． 万柏坤，王瑞平，选择性冠状动脉造影图像边缘提取算法的研究，
天津市图像图形学学会 2001 年年会，2001 年 10 月，106-109
11． 王瑞平，万柏坤，乳腺 X 片微钙化点的自动检测技术，天津生物医
学工程 2000 年会，天津, 2001，12-13