基于三维人脸特征的计算机辅助疾病诊断技术研究
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摘要
近年来,随着计算机技术和医学研究的不断发展,计算机辅助诊断(ComputerAided Diagnosis,CAD)已经成为医学临床诊断的一个重要工具,在某种程度上已经成为现代化医疗的行业标准。目前,CAD已经从诊断的辅助参考逐渐向计算机自动诊断(Automated Computer Diagnosis,ACD)发展。
现代医学研究表明,基因遗传综合症(Genetic Syndrome,GS)是导致儿童精神发育迟滞(Mental Retardation)的主要原因之一,而多数综合症都会导致人的脸部形态发生微妙变化,这使得利用计算机进行GS诊断成为可能和必要。本文目标是解决GS计算机辅助诊断中的若干关键技术问题,并构造面向计算机和医学领域交叉的体系结构框架。
从三维人脸模型中提取特征是GS诊断的一个重要问题,由于本文研究背景的特殊性,特征提取算法面临两个主要问题:1)由于难以获取正对的三维人脸模型,需要在非正对条件下定位模型;2)GS通常会导致脸部特征形态变化,需要研究同时适用于正常人和GS患者的提取算法。针对上述问题,本文提出了NIFE人脸特征提取算法。算法首先根据鼻尖的几何形状和对称性筛选并获取鼻尖及相应的对称平面位置;随后,利用鼻尖和对称平面校准模型至基本正对;最后,根据其他特征点所在区域的曲率及其相对于鼻尖的位置关系,分割特征区域并提取特征点。NIFE算法利用鼻尖在几何形态上的稳定性,通过分布处理确保了对正常人和GS患者都能有效提取特征。实验结果表明,NIFE算法能在模型朝向和位置未知的情况下提取人脸特征,具有较强的鲁棒性,运行速度较快,准确率较高。
智能推理模型是诊断的核心,模型的选择需要在保证准确率的基础上提高泛化能力。由于实验数据是正常人样本和已确诊的病例,因此采用监督学习算法是一个较好的选择。第三章介绍了几种典型的机器学习算法,针对诊断的特点和要求,从样本质量、精度要求以及先验知识的引入三个方面分析对比各种算法,提出以支持向量机(Support Vector Machine,SVM)作为诊断系统的推理核心。随后,在详细介绍SVM算法及其理论依据的基础上,进一步阐明了该算法作为智能推理模型的优势,并讨论了利用SVM解决多类分类问题的方法。
为了提高诊断推理的效果,需要充分利用医学诊断先验知识,将其引入学习过程。针对这一目标,需要研究确定ACD的体系结构。第四章首先介绍了CAD/ACD体系结构的研究现状,指出现有的体系结构无法灵活的适应外部知识的变化。针对该问题,论文提出了一个面向计算机和医学领域交叉的ACD体系结构,其主要特点是引入自然语言处理技术,使得医学专家能够利用诊断指令,自主的将诊断知识结合到诊断体系结构中。论文阐述了自然语言处理的相关技术,分析构造了面向医学诊断指令的产生式,并实现了一个诊断指令解析过程。在此基础上,提出并实现了一种将先验知识引入机器学习的方法,该方法的特点体现在样本筛选和输入数据调整两个方面。通过对诊断指令的处理,将不符合语义描述的样本去除(样本筛选),减少噪音数据对学习的影响;增加与语义相关的维度(数据调整),增强分类器对样本语义的敏感。实验结果证明,该方法能有效提高学习效率,同时也证明了所提出体系结构的可行性。
由于论文中的数据来自不同的单位,样本标注缺乏严谨和一致性,因此存在同类样本分属不同子类的情况,即同类样本中存在一定的“子差异”。针对这一问题,论文提出了一种基于子空间划分的分类算法GBSVM。该算法的最大特点在于通过聚类将同类样本中具有不同“子差异”的样本预先分组,并利用样本筛选和分类器的构造避免这种“子差异”影响分类结果。实验结果表明,相对于普通的SVM算法和不对称打包SVM算法,GBSVM算法有效的提高了多种样本组成混合分类情况下的学习效率。
在论文的最后,介绍了一个儿童遗传综合症诊断原型系统。该原型系统采用了分层次、模块化的结构设计,融合了本文理论和技术研究的成果,实现从数据获取到智能推理的整个过程,具有实用性和参考价值。
With the rapid development of computer science and medical research, Computer Aided Diagnosis (CAD) has become an important or even clinical standard tool for disease diagnosis. Now, the research of CAD is targeting the developing of Automated Computer Diagnosis (ACD).
Genetic research shows that Genetic Syndromes (GS) are one of the main reasons, which lead to mental retardation. The syndromes will usually cause the change of facial morphology, and this makes it possible and necessary to diagnosis genetic syndromes with computer. The aim of this paper is to look into some important issues of CAD research and develop the system architecture with respect to this interdisciplinary topic.
To extract facial features from facial 3D model is an important issue in CAD research. On considering of the application domain, the extraction needs to solve two problems: i) it is difficult to acquire frontal images, so that face orientation is required to be extracted on non-frontal 3D images; ii) normal extraction algorithms are not fit for the change of facial morphology, a solution is needed to deal with both normal and abnormal faces. To address the problems, this thesis presents a Nose Identification-based Feature Extraction (NIFE). The extraction algorithm starts by identifying the position of nose tip and its corresponding symmetry plane according to geometry characteristics. After that, 3D models are adjusted to be frontal based on the results of nose identification. Finally, facial regions are segmented by curvature parameters and the position to the nose tip, and then, feature points are extracted in their corresponding facial regions. NIFE is applicable on both normal and abnormal faces by using nose tip, which is stable in shape, as a reference point. The experiment results show that NIFE is fast and efficient to extract facial features on non-frontal 3D models
Intelligent reasoning is the core of an ACD system. It must be ensured that the reasoning algorithm is accurate and has the ability of generalization. Because the experiment data is labeled, a supervised learning algorithm is a proper choice. With respect to the special requirements of ACD, chapter 3 introduces some typical machine learning algorithms. By analyzing these algorithms from the angles of the sample quality, algorithm accuracy and the incorporating of prior knowledge, the thesis chooses Support Vector Machine (SVM) as the engine of intelligent reasoning. The static learning theory and the implementation of multi-classification are discussed to show the validness of the choice.
Incorporating prior knowledge is an efficient way to improve the accuracy of machine learning. To incorporate prior knowledge, we have to develop a proper diagnosis schema. Chapter 4 first gives a brief overview on state-of-art CAD/ACD schemas, and then arguing that current schemas are not able to meet the interdisciplinary requirement of CAD/ACD, and cannot accommodate the change of diagnosis knowledge. To address the problem, this thesis proposes an interdisciplinary-oriented ACD schema. The main idea is to incorporate medical expert in diagnosis schema by employing Natural Language Processing (NLP) so that prior knowledge can be incorporated as medical diagnosis instructions. To deal with the medical diagnosis instructions, production rules are proposed and implemented based on the discussion of NLP. At the end of this chapter, the thesis proposes an approach to incorporate prior knowledge. The incorporation acts on both sample selection and feature vector generation. With the interpreted prior knowledge, irrelevant samples are eliminated and feature vectors can be expanded with relevant dimensions. According to the experiment results, both of the operations are effective and the feasibility of the schema is proved.
Because the experiment data comes from different institutions, the labels of training samples are not consistent. The inconsistent labeling brings a sub-separable problem. That is, samples with the same label may represent different sub classes. To address the problem, a Grouped Bagging SVM (GBSVM) is proposed. The GBSVM uses cluster algorithms to regroup the sub-separable samples and then construct sub classification SVM with an even sample selection procedure, which makes sure that the sub-separable samples with the same label will not be separated. On compared with ABSVM and normal SVM, GBSVM is more accurate and effective on the experimentdata.
The last chapter of the thesis presents an ACD prototype system. The system isdesigned in a multi-layer manner with the theories and techniques applied. The entirediagnosis process is implemented including data capture and intelligent reasoning, andthe system is highly practical.
现代医学研究表明,基因遗传综合症(Genetic Syndrome,GS)是导致儿童精神发育迟滞(Mental Retardation)的主要原因之一,而多数综合症都会导致人的脸部形态发生微妙变化,这使得利用计算机进行GS诊断成为可能和必要。本文目标是解决GS计算机辅助诊断中的若干关键技术问题,并构造面向计算机和医学领域交叉的体系结构框架。
从三维人脸模型中提取特征是GS诊断的一个重要问题,由于本文研究背景的特殊性,特征提取算法面临两个主要问题:1)由于难以获取正对的三维人脸模型,需要在非正对条件下定位模型;2)GS通常会导致脸部特征形态变化,需要研究同时适用于正常人和GS患者的提取算法。针对上述问题,本文提出了NIFE人脸特征提取算法。算法首先根据鼻尖的几何形状和对称性筛选并获取鼻尖及相应的对称平面位置;随后,利用鼻尖和对称平面校准模型至基本正对;最后,根据其他特征点所在区域的曲率及其相对于鼻尖的位置关系,分割特征区域并提取特征点。NIFE算法利用鼻尖在几何形态上的稳定性,通过分布处理确保了对正常人和GS患者都能有效提取特征。实验结果表明,NIFE算法能在模型朝向和位置未知的情况下提取人脸特征,具有较强的鲁棒性,运行速度较快,准确率较高。
智能推理模型是诊断的核心,模型的选择需要在保证准确率的基础上提高泛化能力。由于实验数据是正常人样本和已确诊的病例,因此采用监督学习算法是一个较好的选择。第三章介绍了几种典型的机器学习算法,针对诊断的特点和要求,从样本质量、精度要求以及先验知识的引入三个方面分析对比各种算法,提出以支持向量机(Support Vector Machine,SVM)作为诊断系统的推理核心。随后,在详细介绍SVM算法及其理论依据的基础上,进一步阐明了该算法作为智能推理模型的优势,并讨论了利用SVM解决多类分类问题的方法。
为了提高诊断推理的效果,需要充分利用医学诊断先验知识,将其引入学习过程。针对这一目标,需要研究确定ACD的体系结构。第四章首先介绍了CAD/ACD体系结构的研究现状,指出现有的体系结构无法灵活的适应外部知识的变化。针对该问题,论文提出了一个面向计算机和医学领域交叉的ACD体系结构,其主要特点是引入自然语言处理技术,使得医学专家能够利用诊断指令,自主的将诊断知识结合到诊断体系结构中。论文阐述了自然语言处理的相关技术,分析构造了面向医学诊断指令的产生式,并实现了一个诊断指令解析过程。在此基础上,提出并实现了一种将先验知识引入机器学习的方法,该方法的特点体现在样本筛选和输入数据调整两个方面。通过对诊断指令的处理,将不符合语义描述的样本去除(样本筛选),减少噪音数据对学习的影响;增加与语义相关的维度(数据调整),增强分类器对样本语义的敏感。实验结果证明,该方法能有效提高学习效率,同时也证明了所提出体系结构的可行性。
由于论文中的数据来自不同的单位,样本标注缺乏严谨和一致性,因此存在同类样本分属不同子类的情况,即同类样本中存在一定的“子差异”。针对这一问题,论文提出了一种基于子空间划分的分类算法GBSVM。该算法的最大特点在于通过聚类将同类样本中具有不同“子差异”的样本预先分组,并利用样本筛选和分类器的构造避免这种“子差异”影响分类结果。实验结果表明,相对于普通的SVM算法和不对称打包SVM算法,GBSVM算法有效的提高了多种样本组成混合分类情况下的学习效率。
在论文的最后,介绍了一个儿童遗传综合症诊断原型系统。该原型系统采用了分层次、模块化的结构设计,融合了本文理论和技术研究的成果,实现从数据获取到智能推理的整个过程,具有实用性和参考价值。
With the rapid development of computer science and medical research, Computer Aided Diagnosis (CAD) has become an important or even clinical standard tool for disease diagnosis. Now, the research of CAD is targeting the developing of Automated Computer Diagnosis (ACD).
Genetic research shows that Genetic Syndromes (GS) are one of the main reasons, which lead to mental retardation. The syndromes will usually cause the change of facial morphology, and this makes it possible and necessary to diagnosis genetic syndromes with computer. The aim of this paper is to look into some important issues of CAD research and develop the system architecture with respect to this interdisciplinary topic.
To extract facial features from facial 3D model is an important issue in CAD research. On considering of the application domain, the extraction needs to solve two problems: i) it is difficult to acquire frontal images, so that face orientation is required to be extracted on non-frontal 3D images; ii) normal extraction algorithms are not fit for the change of facial morphology, a solution is needed to deal with both normal and abnormal faces. To address the problems, this thesis presents a Nose Identification-based Feature Extraction (NIFE). The extraction algorithm starts by identifying the position of nose tip and its corresponding symmetry plane according to geometry characteristics. After that, 3D models are adjusted to be frontal based on the results of nose identification. Finally, facial regions are segmented by curvature parameters and the position to the nose tip, and then, feature points are extracted in their corresponding facial regions. NIFE is applicable on both normal and abnormal faces by using nose tip, which is stable in shape, as a reference point. The experiment results show that NIFE is fast and efficient to extract facial features on non-frontal 3D models
Intelligent reasoning is the core of an ACD system. It must be ensured that the reasoning algorithm is accurate and has the ability of generalization. Because the experiment data is labeled, a supervised learning algorithm is a proper choice. With respect to the special requirements of ACD, chapter 3 introduces some typical machine learning algorithms. By analyzing these algorithms from the angles of the sample quality, algorithm accuracy and the incorporating of prior knowledge, the thesis chooses Support Vector Machine (SVM) as the engine of intelligent reasoning. The static learning theory and the implementation of multi-classification are discussed to show the validness of the choice.
Incorporating prior knowledge is an efficient way to improve the accuracy of machine learning. To incorporate prior knowledge, we have to develop a proper diagnosis schema. Chapter 4 first gives a brief overview on state-of-art CAD/ACD schemas, and then arguing that current schemas are not able to meet the interdisciplinary requirement of CAD/ACD, and cannot accommodate the change of diagnosis knowledge. To address the problem, this thesis proposes an interdisciplinary-oriented ACD schema. The main idea is to incorporate medical expert in diagnosis schema by employing Natural Language Processing (NLP) so that prior knowledge can be incorporated as medical diagnosis instructions. To deal with the medical diagnosis instructions, production rules are proposed and implemented based on the discussion of NLP. At the end of this chapter, the thesis proposes an approach to incorporate prior knowledge. The incorporation acts on both sample selection and feature vector generation. With the interpreted prior knowledge, irrelevant samples are eliminated and feature vectors can be expanded with relevant dimensions. According to the experiment results, both of the operations are effective and the feasibility of the schema is proved.
Because the experiment data comes from different institutions, the labels of training samples are not consistent. The inconsistent labeling brings a sub-separable problem. That is, samples with the same label may represent different sub classes. To address the problem, a Grouped Bagging SVM (GBSVM) is proposed. The GBSVM uses cluster algorithms to regroup the sub-separable samples and then construct sub classification SVM with an even sample selection procedure, which makes sure that the sub-separable samples with the same label will not be separated. On compared with ABSVM and normal SVM, GBSVM is more accurate and effective on the experimentdata.
The last chapter of the thesis presents an ACD prototype system. The system isdesigned in a multi-layer manner with the theories and techniques applied. The entirediagnosis process is implemented including data capture and intelligent reasoning, andthe system is highly practical.
引文
[1] B Van Ginneken, BM Ter Haar Romeny, MA Viergever. Computer-aided diagnosis in chest radiography: a survey, IEEE Transaction Medical Imaging, 2001, 20:1228-1241.
[2] Engle RL. Attempt to use computers as diagnostic aids in medical decision making: a 30-year experience. Perspectives in Biology and Medicine, 1992,35(2):207-218.
[3] Y. Reisman. Computer-based clinical decision aids: A review of methods and assessment of systems. Informatics for Health and Social Care, 1996, 21(3): 179-197.
[4] K Doi, Computer-aided diagnosis in medical imaging: Historical review, current status and future potential, Computerized Medical Imaging and Graphics, 2007, 31(4).198-211.
[5] Hammond P, Hutton TJ, Allanson JE, Campbell LE, Hennekam RCM, Holden S. Murphy KC, Patton MA, Shaw A, Temple IK, Trotter M, Winter RM. 3D analysis of facial morphology. Am J Med Genet A, 2004, 126:339-348.
[6] Hammond P, Hutton TJ, Allanson JE. Discriminating Power of Localized Three-Dimensional Facial Morphology. Am J Hum Genet, 2005, 77(6):999-1010.
[7] K.K. Sung, T. Poggio. Learning human face detection in cluttered scenes. Computer Analysis of Images and Patterns, Springer-Verlag, New York, 1995,432-439.
[8] F. Smeraldi, O. Carmona, J. Bigun. Saccadic search with Gabor features applied to eye detection and real-time head tracking. Image and Vision Computation, 2000, 18(4):323-329.
[9] R. Brunelli, T. Poggio. Face recognition: features versus templates. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1993, 15(10): 1042-1052.
[10] P.J. Phillips, P. Grother, R.J. Michaels, D.M. Blackburn, E. Tabassi, J. Bone, FRVT 2002: overview and summary.
[11] B. Gokberk, M. Irfanoglu, L. Akarun. 3D shape-based face representation and feature extraction for face recognition. Image and Version Computing, 2006.24:857-869.
[12] D. Colbry, G. Stockman and A. Jain. Detection of Anchor Points for 3D Face Verification. In proceedings of IEEE Workshop on Advanced 3D Imaging for Safety and Security A3DISS, SanDiego, CA, June 25,2005, 118-118.
[13] Gordon G. Face recognition based on depth and curvature features. CVPR, 1992, 108-110.
[14] Colombo A, Cusano C, Schettini R. 3D face detection using curvature analysis. Pattern Recognition, 2006, 39(3):444-45.
[15] Y Lee, H Song, U Yang, H Shin, K Sohn. Local feature based 3D face recognition. Lecture Notes in Computer Science. 2005, 3546:909-918.
[16] Kevin W. Bowyer, Kyong Chang and Patrick Flynn. A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition. Computer Vision and Image Understanding, 2006, 101(1):1-15
[17] K. Chang, K. Bowyer and P. Flynn. An evaluation of multi-modal 2D + 3D face biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 619-624.
[18] C. Hesher, A. Srivastava, and G. Erlebacher. A Novel Technique for Face Recognition Using Range Imaging. Inter Multi-conference in Computer Science, 2002, 2:201-204.
[19] Y. Lee, K. Park, J. Shim, and T. Yi. 3D Face Recognition Using Statistical Multiple Features for the Local Depth Information. In proceedings of 16th International Conference on Vision Interface, 2003,429-434.
[20] Chenghua Xu, Yunhong Wang, Tieniu Tan, Long Quan. Robust nose detection in 3D facial data using local characteristics. IEEE International Conference on Image Processing, 2004, 3:1995-1998.
[21] Chenghua Xu, Tieniu Tan, Yunhong Wang and Long Quan. Combining local features for robust nose location in 3D facial data. Pattern Recognition Letters, 2006, 27(13):1487-1494.
[22] N Cristianini, J Shawe-Taylor. An introduction to support Vector Machines: and other kernel-based learning methods. 1999, Cambridge University Press New York, NY, USA.
[23] Vapnik V N. Statistical Learning Theory. New York: Wiley, 1998.
[24] Vapnik V N. The Nature of Statistical Learning Theory. 2nd edition. New York: Springer-Verlag, 1999.
[25] Cortes C, Vapnik V N. Support vector networks. Machine Learning, 1995. 20(3): 273-297.
[26] Vapnik V N, Golowich S, Smola A. Support vector method for function approximation, regression estimation and signal processing. Advances in Neural Information Processing Systems. Cambridge: MIT Press, 1997. 281-287.
[27] VN Vapnik. An overview of statistical learning theory. IEEE Transactions on Neural Networks, 1999, 10(5):988-999.
[28] 边肇祺,张学工. 模式识别 (第二版). 北京：清华大学出版社,2000.
[29] L. Devroye, L. Gyorfi, and G. Lugosi. A Probability Theory of Pattern Recognition. New York: Springer-Verlag, 1996.
[30] A. B. Novikoff. On convergence proofs on perceptrons. In symposium on the Mathematical Theory of Automata, 12:615-622.
[31] Brailovsky V.L., Barzilay O., et al. On global, local, mixed and neighborhood kernels for support vector machines. Pattern recognition letters, 1999,20:1183-1190.
[32] Cristianini N., Campbell C., et al. kernel methods: current research and future directions. Machine Learning, 2002,46:5-9.
[33] Chiang L.H., Kotanchek M.E., et al. Fault diagnosis based on Fisher discriminant analysis and support vector machines. Computers and chemical engineering, 2004, 28:1389-1401.
[34] Kikuchi T. and Abe S. Comparison between error correcting output codes and fuzzy support vector machines. Pattern recognition letters, 2005,26(12):1937-1945.
[35] Weston J. and Watkins C. Support vector machines for multi-class pattern recognition. In proceedings of the seventh european symposium on artificial neural networks, 1999.
[36] Krebel U.H-G. Pairwise classification and support vector machines. Advances in Kernel Methods: Support Vector Learning, 1999, 255-268.
[37] Weston J., Mukherjee S., et al. Feature selection for SVMs. Advances in Neural Information Processing Systems, 2001.
[38] Valentini GGene expression data analysis of human lymphoma using support vector machines and output coding ensembles. Artificial Intelligence in medicine, 2002, 26:281-304.
[39] Windeatt T. and Ghaderi R.Coding and decoding strategies for multi-class learning problems. Information fusion, 2003,4:11-21.
[40] Abe S. and Inoue T. Fuzzy support vector machines for multi-class problems. European symposium on artificial neural networks, 2002.
[41] Platt J.C. Training of support vector machines using sequential minimal optimization. Advances in kernel method - support vector learning. MIT Press, 1999, 185-208.
[42] Suykens J. and Vandewale J. Least squares support vector machine classifiers. Neural processing letters, 1999,3:293-300.
[43] Chapelle O., Vapnik V., et al. Choosing kernel parameters for support vector machines. Machine learning, 2002,46:131-159.
[44] K. Doi, Overview on research and development of computer-aided diagnostic schemes, Seminars in Ultrasound, CT, and MRI, 2004, Volume 25, Issue 5,404-410.
[45] Kunio Doi. Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Computerized Medical Imaging and Graphics, 2007, 31(4-5):198-211.
[46] M. Brown, M. McNitt-Gray, R. Pais, S. Shah, P. Qing, I. Da Costa, D. Aberle and J. Goldin, CAD in clinical trials: Current role and architectural requirements, Computerized Medical Imaging and Graphics, 2007,31(4):332-337.
[47] Li Q, Sone S, Doi K. Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. Med Phys, 2003, 30:2040-2051.
[48] Paik DS, Beaulieu CF, Rubin GD, Acar B, Jeffrey RB, Yee J, et al. Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT. IEEE Trans Med Imag, 2004, 23:661-75.
[49] Bae KT, Kim JS, Na YH, Kim KG, Kim JH. Pulmonary nodules: automated detection on CT images with morphologic matching algorithm-preliminary results.Radiology,2005,236:286-294.
[50]Zhao BS,Gamsu G,Ginsburg MS,Jiang L,Schwartz LH.Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm.Journal of Applied Clinical Medical Physics,2003,4:248-260.
[51]McCulloch CC,Kaucic RA,Mendonca PRS,Walter D J,Avila RS.Model based detection of lung nodules in computed tomography exams.Academic Radiology,2004,11:258-266.
[52]J Shiraishi,Q Li,D Appelbaum,Y Pu,K Doi.Development of a computer-aided diagnostic scheme for detection of interval changes in successive whole-body bone scans.Medical Physics,2007,34(1):25-36.
[53]Qiang Li,Shigehiko Katsuragawa,and Kunio Doi.Computer-aided diagnostic scheme for lung nodule detection in digital chest radiographs by use of a multiple-template matching technique.Medical Physics.2001,28(10):2070-2076.
[54]王显芳,杜利民.利用覆盖歧义检测法和统计语言模型进行汉语自动分词.电子与信息学报,2003,25(9):1168-1173.
[55]孙茂松,邹嘉彦.汉语自动分词研究评述.当代语言学,2001,(1):163-190.
[56]刘群,张华平,俞鸿魁,程学旗.基于层叠隐马模型的汉语词法分析.计算机研究与发展,2004,41(8):1421-1429.
[57]Dietterich,T.G.,Machine-Learning Research:Four Current Directions.American Association for Artificial Intelligence,1997.
[58]Fabien Lauer and Gerard Bloch.Incorporating prior knowledge in support vector machines for classification:A review.Neurocomputing,2008,71(7-9):1578-1594.
[59]D.Decoste,B.Scholkopf,Training invariant support vector machines,Mach.Learn,2002,46:161-190.
[60]B.Haasdonk,D.Keysers,Tangent distance kernels for support vector machines.In proceedings of the 16th International Conference on Pattern Recognition,2002,2:864-868.
[61]H.Schulz-Mirbach,Constructing invariant features by averaging techniques.In proceedings of the 12th International Conference on Pattern Recognition,1994,2:387-390.
[62]L.Wang,Y.Gao,K.L.Chan,P.Xue,W.-Y.Yau,Retrieval with knowledge-driven kernel design:an approach to improving SVM based CBIR with relevance feedback.In proceedings of the International Conference on Computer Vision,IEEE Computer Society,2005,1355-1362.
[63]B.Scholkopf,P.Simard,A.J.Smola,V.Vapnik,Prior knowledge in support vector kernels,Advances in Neural Information Processing Systems,The MIT Press,Cambridge,MA,USA, 1998, 640-646.
[64] G. Loosli, S. Canu, S.V.N. Vishwanathan, A.J. Smola, Invariances in classification: an efficient SVM implementation. In proceedings of International Symposium on Applied Stochastic Models and Data Analysis, 2005.
[65] P. Shivaswamy, T. Jebara, Permutation invariant SVMs. In proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA, 2006.
[66] T. Poggio, T. Vetter, Recognition and structure from one 2D model view: Observations on prototypes, object classes and symmetries, Tech. Rep. AIM-1347, Massachusetts Institute of Technology, Cambridge, MA, USA, 1992.
[67] Y.S. Abu-Mostafa, Learning from hints in neural networks,J. Complexity, 1992, 6(2):192-198.
[68] Y.S. Abu-Mostafa, Learning from hints, J. Complexity, 1994,10(1):165-178.
[69] B. Scholkopf, C. Burges, V. Vapnik. Incorporating invariances in support vector learning machines. in Proceedings of the International Conference on Artificial Neural Networks, Lecture Notes in Computer Science, 1996, 1112:47-52.
[70] Kuhl, A., Kruger, L., Wohler, C., Kressel, U. Training of classifiers using virtual samples only. ICPR, 2004, 3:418-421.
[71] T. Joachims. Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms. Kluwer Academic Publishers, 2002.
[72] T. Joachims. Transductive inference for text classification using support vector machines. ICML, Morgan Kaufmann, 1999,200-209.
[73] Wu, X. and R. Srihari. Incorporating Prior Knowledge with Weighted Margin Support Vector Machines. In proceedings of KDD, 2004.
[74] X. Wu, R. Srihari, Incorporating prior knowledge with weighted margin support vector machines, in: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA, ACM Press, New York, NY, USA,2004, 326-333.
[75] L.Wang, P. Xue, K. L. Chan, Incorporating prior knowledge into SVM for image retrieval. In proceedings of the 17th Int. Conf. on Pattern Recognition, Cambridge, UK, 2004, 981-984.
[76] Paul Sajda. Machine Learning for Detection and Diagnosis of Disease. Annual Review of Biomedical Engineering, 2006, 8:537-565.
[77] T. K. Ho. Random decision forests. Proceedings of 3rd International Conference of Document Analysis and Recognition, 1995, 278-282.
[78] T. K. Ho. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20:832-844.
[79] T. K. Ho, J.J. Hull and S.N. Srihari. Decision Combination in Multiple Classifier Systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(1):66-75.
[80] Dacheng Tao, Xiaoou Tang, Xuelong Li and Xindong Wu. Asymmetric Bagging and Random Subspace for Support Vector Mahcines-Based Relevance Feedback in Image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2006, 28(7): 1088-1099.
[81] Nicolas Garcia-Pedrajas and Domingo Ortiz-Boyer. Boosting Random Subspace Method. Neural Networks, 2008,21:1344-1362.
[82] Zhen Zhang, Rongrong Ji, Hongxun Yao, Pengfei Xu, Jicheng Wang. Random Sampling SVM Based Soft Query Expansion for Image Retrieval. In proceedings of International Conference on Image and Graphics. 2007.
[83] Dacheng Tao, Xuelong Li and Stephen J. Maybank. Negative Samples Analysis in Relevance Feedback. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(4):568-580.
[2] Engle RL. Attempt to use computers as diagnostic aids in medical decision making: a 30-year experience. Perspectives in Biology and Medicine, 1992,35(2):207-218.
[3] Y. Reisman. Computer-based clinical decision aids: A review of methods and assessment of systems. Informatics for Health and Social Care, 1996, 21(3): 179-197.
[4] K Doi, Computer-aided diagnosis in medical imaging: Historical review, current status and future potential, Computerized Medical Imaging and Graphics, 2007, 31(4).198-211.
[5] Hammond P, Hutton TJ, Allanson JE, Campbell LE, Hennekam RCM, Holden S. Murphy KC, Patton MA, Shaw A, Temple IK, Trotter M, Winter RM. 3D analysis of facial morphology. Am J Med Genet A, 2004, 126:339-348.
[6] Hammond P, Hutton TJ, Allanson JE. Discriminating Power of Localized Three-Dimensional Facial Morphology. Am J Hum Genet, 2005, 77(6):999-1010.
[7] K.K. Sung, T. Poggio. Learning human face detection in cluttered scenes. Computer Analysis of Images and Patterns, Springer-Verlag, New York, 1995,432-439.
[8] F. Smeraldi, O. Carmona, J. Bigun. Saccadic search with Gabor features applied to eye detection and real-time head tracking. Image and Vision Computation, 2000, 18(4):323-329.
[9] R. Brunelli, T. Poggio. Face recognition: features versus templates. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1993, 15(10): 1042-1052.
[10] P.J. Phillips, P. Grother, R.J. Michaels, D.M. Blackburn, E. Tabassi, J. Bone, FRVT 2002: overview and summary.
[11] B. Gokberk, M. Irfanoglu, L. Akarun. 3D shape-based face representation and feature extraction for face recognition. Image and Version Computing, 2006.24:857-869.
[12] D. Colbry, G. Stockman and A. Jain. Detection of Anchor Points for 3D Face Verification. In proceedings of IEEE Workshop on Advanced 3D Imaging for Safety and Security A3DISS, SanDiego, CA, June 25,2005, 118-118.
[13] Gordon G. Face recognition based on depth and curvature features. CVPR, 1992, 108-110.
[14] Colombo A, Cusano C, Schettini R. 3D face detection using curvature analysis. Pattern Recognition, 2006, 39(3):444-45.
[15] Y Lee, H Song, U Yang, H Shin, K Sohn. Local feature based 3D face recognition. Lecture Notes in Computer Science. 2005, 3546:909-918.
[16] Kevin W. Bowyer, Kyong Chang and Patrick Flynn. A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition. Computer Vision and Image Understanding, 2006, 101(1):1-15
[17] K. Chang, K. Bowyer and P. Flynn. An evaluation of multi-modal 2D + 3D face biometrics. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 619-624.
[18] C. Hesher, A. Srivastava, and G. Erlebacher. A Novel Technique for Face Recognition Using Range Imaging. Inter Multi-conference in Computer Science, 2002, 2:201-204.
[19] Y. Lee, K. Park, J. Shim, and T. Yi. 3D Face Recognition Using Statistical Multiple Features for the Local Depth Information. In proceedings of 16th International Conference on Vision Interface, 2003,429-434.
[20] Chenghua Xu, Yunhong Wang, Tieniu Tan, Long Quan. Robust nose detection in 3D facial data using local characteristics. IEEE International Conference on Image Processing, 2004, 3:1995-1998.
[21] Chenghua Xu, Tieniu Tan, Yunhong Wang and Long Quan. Combining local features for robust nose location in 3D facial data. Pattern Recognition Letters, 2006, 27(13):1487-1494.
[22] N Cristianini, J Shawe-Taylor. An introduction to support Vector Machines: and other kernel-based learning methods. 1999, Cambridge University Press New York, NY, USA.
[23] Vapnik V N. Statistical Learning Theory. New York: Wiley, 1998.
[24] Vapnik V N. The Nature of Statistical Learning Theory. 2nd edition. New York: Springer-Verlag, 1999.
[25] Cortes C, Vapnik V N. Support vector networks. Machine Learning, 1995. 20(3): 273-297.
[26] Vapnik V N, Golowich S, Smola A. Support vector method for function approximation, regression estimation and signal processing. Advances in Neural Information Processing Systems. Cambridge: MIT Press, 1997. 281-287.
[27] VN Vapnik. An overview of statistical learning theory. IEEE Transactions on Neural Networks, 1999, 10(5):988-999.
[28] 边肇祺,张学工. 模式识别 (第二版). 北京：清华大学出版社,2000.
[29] L. Devroye, L. Gyorfi, and G. Lugosi. A Probability Theory of Pattern Recognition. New York: Springer-Verlag, 1996.
[30] A. B. Novikoff. On convergence proofs on perceptrons. In symposium on the Mathematical Theory of Automata, 12:615-622.
[31] Brailovsky V.L., Barzilay O., et al. On global, local, mixed and neighborhood kernels for support vector machines. Pattern recognition letters, 1999,20:1183-1190.
[32] Cristianini N., Campbell C., et al. kernel methods: current research and future directions. Machine Learning, 2002,46:5-9.
[33] Chiang L.H., Kotanchek M.E., et al. Fault diagnosis based on Fisher discriminant analysis and support vector machines. Computers and chemical engineering, 2004, 28:1389-1401.
[34] Kikuchi T. and Abe S. Comparison between error correcting output codes and fuzzy support vector machines. Pattern recognition letters, 2005,26(12):1937-1945.
[35] Weston J. and Watkins C. Support vector machines for multi-class pattern recognition. In proceedings of the seventh european symposium on artificial neural networks, 1999.
[36] Krebel U.H-G. Pairwise classification and support vector machines. Advances in Kernel Methods: Support Vector Learning, 1999, 255-268.
[37] Weston J., Mukherjee S., et al. Feature selection for SVMs. Advances in Neural Information Processing Systems, 2001.
[38] Valentini GGene expression data analysis of human lymphoma using support vector machines and output coding ensembles. Artificial Intelligence in medicine, 2002, 26:281-304.
[39] Windeatt T. and Ghaderi R.Coding and decoding strategies for multi-class learning problems. Information fusion, 2003,4:11-21.
[40] Abe S. and Inoue T. Fuzzy support vector machines for multi-class problems. European symposium on artificial neural networks, 2002.
[41] Platt J.C. Training of support vector machines using sequential minimal optimization. Advances in kernel method - support vector learning. MIT Press, 1999, 185-208.
[42] Suykens J. and Vandewale J. Least squares support vector machine classifiers. Neural processing letters, 1999,3:293-300.
[43] Chapelle O., Vapnik V., et al. Choosing kernel parameters for support vector machines. Machine learning, 2002,46:131-159.
[44] K. Doi, Overview on research and development of computer-aided diagnostic schemes, Seminars in Ultrasound, CT, and MRI, 2004, Volume 25, Issue 5,404-410.
[45] Kunio Doi. Computer-aided diagnosis in medical imaging: Historical review, current status and future potential. Computerized Medical Imaging and Graphics, 2007, 31(4-5):198-211.
[46] M. Brown, M. McNitt-Gray, R. Pais, S. Shah, P. Qing, I. Da Costa, D. Aberle and J. Goldin, CAD in clinical trials: Current role and architectural requirements, Computerized Medical Imaging and Graphics, 2007,31(4):332-337.
[47] Li Q, Sone S, Doi K. Selective enhancement filters for nodules, vessels, and airway walls in two- and three-dimensional CT scans. Med Phys, 2003, 30:2040-2051.
[48] Paik DS, Beaulieu CF, Rubin GD, Acar B, Jeffrey RB, Yee J, et al. Surface normal overlap: a computer-aided detection algorithm with application to colonic polyps and lung nodules in helical CT. IEEE Trans Med Imag, 2004, 23:661-75.
[49] Bae KT, Kim JS, Na YH, Kim KG, Kim JH. Pulmonary nodules: automated detection on CT images with morphologic matching algorithm-preliminary results.Radiology,2005,236:286-294.
[50]Zhao BS,Gamsu G,Ginsburg MS,Jiang L,Schwartz LH.Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm.Journal of Applied Clinical Medical Physics,2003,4:248-260.
[51]McCulloch CC,Kaucic RA,Mendonca PRS,Walter D J,Avila RS.Model based detection of lung nodules in computed tomography exams.Academic Radiology,2004,11:258-266.
[52]J Shiraishi,Q Li,D Appelbaum,Y Pu,K Doi.Development of a computer-aided diagnostic scheme for detection of interval changes in successive whole-body bone scans.Medical Physics,2007,34(1):25-36.
[53]Qiang Li,Shigehiko Katsuragawa,and Kunio Doi.Computer-aided diagnostic scheme for lung nodule detection in digital chest radiographs by use of a multiple-template matching technique.Medical Physics.2001,28(10):2070-2076.
[54]王显芳,杜利民.利用覆盖歧义检测法和统计语言模型进行汉语自动分词.电子与信息学报,2003,25(9):1168-1173.
[55]孙茂松,邹嘉彦.汉语自动分词研究评述.当代语言学,2001,(1):163-190.
[56]刘群,张华平,俞鸿魁,程学旗.基于层叠隐马模型的汉语词法分析.计算机研究与发展,2004,41(8):1421-1429.
[57]Dietterich,T.G.,Machine-Learning Research:Four Current Directions.American Association for Artificial Intelligence,1997.
[58]Fabien Lauer and Gerard Bloch.Incorporating prior knowledge in support vector machines for classification:A review.Neurocomputing,2008,71(7-9):1578-1594.
[59]D.Decoste,B.Scholkopf,Training invariant support vector machines,Mach.Learn,2002,46:161-190.
[60]B.Haasdonk,D.Keysers,Tangent distance kernels for support vector machines.In proceedings of the 16th International Conference on Pattern Recognition,2002,2:864-868.
[61]H.Schulz-Mirbach,Constructing invariant features by averaging techniques.In proceedings of the 12th International Conference on Pattern Recognition,1994,2:387-390.
[62]L.Wang,Y.Gao,K.L.Chan,P.Xue,W.-Y.Yau,Retrieval with knowledge-driven kernel design:an approach to improving SVM based CBIR with relevance feedback.In proceedings of the International Conference on Computer Vision,IEEE Computer Society,2005,1355-1362.
[63]B.Scholkopf,P.Simard,A.J.Smola,V.Vapnik,Prior knowledge in support vector kernels,Advances in Neural Information Processing Systems,The MIT Press,Cambridge,MA,USA, 1998, 640-646.
[64] G. Loosli, S. Canu, S.V.N. Vishwanathan, A.J. Smola, Invariances in classification: an efficient SVM implementation. In proceedings of International Symposium on Applied Stochastic Models and Data Analysis, 2005.
[65] P. Shivaswamy, T. Jebara, Permutation invariant SVMs. In proceedings of the 23rd International Conference on Machine Learning, Pittsburgh, PA, USA, 2006.
[66] T. Poggio, T. Vetter, Recognition and structure from one 2D model view: Observations on prototypes, object classes and symmetries, Tech. Rep. AIM-1347, Massachusetts Institute of Technology, Cambridge, MA, USA, 1992.
[67] Y.S. Abu-Mostafa, Learning from hints in neural networks,J. Complexity, 1992, 6(2):192-198.
[68] Y.S. Abu-Mostafa, Learning from hints, J. Complexity, 1994,10(1):165-178.
[69] B. Scholkopf, C. Burges, V. Vapnik. Incorporating invariances in support vector learning machines. in Proceedings of the International Conference on Artificial Neural Networks, Lecture Notes in Computer Science, 1996, 1112:47-52.
[70] Kuhl, A., Kruger, L., Wohler, C., Kressel, U. Training of classifiers using virtual samples only. ICPR, 2004, 3:418-421.
[71] T. Joachims. Learning to Classify Text Using Support Vector Machines: Methods, Theory and Algorithms. Kluwer Academic Publishers, 2002.
[72] T. Joachims. Transductive inference for text classification using support vector machines. ICML, Morgan Kaufmann, 1999,200-209.
[73] Wu, X. and R. Srihari. Incorporating Prior Knowledge with Weighted Margin Support Vector Machines. In proceedings of KDD, 2004.
[74] X. Wu, R. Srihari, Incorporating prior knowledge with weighted margin support vector machines, in: Proceedings of the 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, USA, ACM Press, New York, NY, USA,2004, 326-333.
[75] L.Wang, P. Xue, K. L. Chan, Incorporating prior knowledge into SVM for image retrieval. In proceedings of the 17th Int. Conf. on Pattern Recognition, Cambridge, UK, 2004, 981-984.
[76] Paul Sajda. Machine Learning for Detection and Diagnosis of Disease. Annual Review of Biomedical Engineering, 2006, 8:537-565.
[77] T. K. Ho. Random decision forests. Proceedings of 3rd International Conference of Document Analysis and Recognition, 1995, 278-282.
[78] T. K. Ho. The random subspace method for constructing decision forests. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20:832-844.
[79] T. K. Ho, J.J. Hull and S.N. Srihari. Decision Combination in Multiple Classifier Systems. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1994, 16(1):66-75.
[80] Dacheng Tao, Xiaoou Tang, Xuelong Li and Xindong Wu. Asymmetric Bagging and Random Subspace for Support Vector Mahcines-Based Relevance Feedback in Image Retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2006, 28(7): 1088-1099.
[81] Nicolas Garcia-Pedrajas and Domingo Ortiz-Boyer. Boosting Random Subspace Method. Neural Networks, 2008,21:1344-1362.
[82] Zhen Zhang, Rongrong Ji, Hongxun Yao, Pengfei Xu, Jicheng Wang. Random Sampling SVM Based Soft Query Expansion for Image Retrieval. In proceedings of International Conference on Image and Graphics. 2007.
[83] Dacheng Tao, Xuelong Li and Stephen J. Maybank. Negative Samples Analysis in Relevance Feedback. IEEE Transactions on Knowledge and Data Engineering, 2007, 19(4):568-580.