基于混合贝叶斯网络的医学图像语义建模及其检索的研究
|
摘要
医学图像语义检索的研究正成为医学图像检索研究的新热点,也是医学迫切需要解决的问题,它是实现医学图像理解的多学科交叉的研究课题,融合了医学、图像处理、模式识别、计算机视觉、机器学习、数据库与人工智能等研究领域。图像语义检索的难点和重点在于语义建模和语义相似度度量,而图像语义建模的核心任务是从反映图像内容的低层视觉特征提取出隐含的、预先未知的高层语义,弥补“语义鸿沟”问题。本文针对医学图像的特点和医学临床的需求,提出了基于混合贝叶斯网络(hybrid Bayesian network, HBN)的医学图像语义建模的方法,分别研究了医学图像语义的多层统计模型、对象语义和高级语义的获取以及语义相似度度量等内容,并将以上方法应用于星形细胞瘤恶性程度的预测,设计了星形细胞瘤恶性程度的语义模型和检索系统。
本论文的主要研究成果及其创新点包括:
1、提出了引入条件高斯模型来模糊离散化连续变量的基于混合贝叶斯网络的医学图像语义建模的方法
(1)考虑到医学图像的特点以及贝叶斯网络的性能和优势,提出了利用贝叶斯网络来对医学图像的语义建模,但传统的贝叶斯网络只适用于离散变量,而自动提取的图像特征往往是连续的,为了可以在贝叶斯网络中使用连续变量,并考虑到医学图像特征的模糊性和不确定性,提出了使用条件高斯(conditional Gaussian, CG)模型对连续的视觉特征进行模糊离散化处理,然后嵌入到贝叶斯网络中,建立仅利用低层视觉特征的智能模型BN-CG-Low。仿真实验结果表明,该模型可以很好地描述图像的内容,从低层视觉特征自动提取高层语义,有效解决“语义鸿沟”问题,并提供了符合医学习惯的知识表达。
(2)在第1(1)点的基础上,考虑到贝叶斯网络的数据融合能力,为了更完整地描述图像内容和提高语义提取的准确率和查全率,提出了融合低层视觉特征和中层语义的语义模型BN-CG,通过与BN-CG-Low的比较实验,BN-CG可以进一步提高准确率和查全率。
2、给出了基于BN-GMM的医学图像的三层语义模型
医学图像的诊断中,医生的思维是着重病变区域的性质和特点,然后综合考虑从不同角度对病变区的理解和判断,最后得出病症语义。从这个特点出发,我们提出了首先利用高斯混合模型(Gaussian mixture models, GMM)对病变区域进行模糊识别,实现从视觉特征到对象语义的映射,然后利用贝叶斯网络融合各种从不同理解角度得到的对象语义,从而建立一个基于BN-GMM的医学图像三层语义模型。与使用K近邻分类器(KNN)代替GMM的BN-KNN相比,取得了更好的精度和语义的可解释性。
3、研究了分层的基于语义概率空间距离的语义相似度度量方法
在前面所提出的语义模型中,不同层次的语义其重要性是不一样的,语义的概率反映了语义的置信度,这也符合医学诊断的习惯,因此提出了按照语义层次的不同进行分层处理,在每一层分别通过度量语义的后验概率空间距离进行语义相似度的度量。将这种度量方法应用于医学图像的语义检索,取得了令人满意的查询结果。
4、设计了基于BN-SVM的医学图像三层语义模型
考虑到临床实际中取得大量具有病理结果的医学图像训练样本的困难,很多研究成果表明,在小样本的情况下,支持向量机(Support vector machine,SVM)能取得比GMM更高的识别精度,因此提出了首先利用支持向量机对病变区域进行识别,实现从低层视觉特征到对象语义的映射,然后利用贝叶斯网络融合各种从不同理解角度得到的对象语义(来源于不同的SVM),从而建立一个适用于小样本的基于BN-SVM的医学图像三层语义模型。实验结果表明,与采用K近邻分类器或GMM取代SVM的贝叶斯网络相比,取得了更好的结果。
在本文所提供的相同图像样本下,本文所提出的三种方法用于医学图象语义建模,均比以往的嵌入KNN的混合贝叶斯网络有着更高的准确率和查全率。
医学图像内容的分层和结构性表达、语义的自动获取、语义相似度的研究为实现能应用于医学临床的不同语义层次的检索提供了条件和基础。
The research of medical image semantic retrieval is a new hotspot in the field of medical image retrieval, and should be settled urgently in medicine, too. It is an interdisciplinary study on medical image understanding, denoting the synergy of medicine, image processing, pattern recognition, computer vision, machine learning, database and artificial intelligence. Semantic modeling and semantic similarity measure are its difficulty and key. The fundamental task of image semantic modeling is to extract implicit unknown high-level semantics in order to make up“semantic gap”. The dissertation tries to study medical image semantic modeling approach based on hybrid Bayesian networks (BN), thought about characters of medical images and demand in medicine. It includes the study of multi-level semantic statistical models of medical images, capture of object semantics and high-level semantics, and semantic similarity measure. To validate these methods, they are applied in the prediction of astrocytoma malignant degree, and semantic models of astrocytoma malignant degree and a semantic retrieval system are designed.
The main contributions of the dissertation are as follows:
1. It proposes a medical image semantic modeling method using hybrid BN embedding Conditional Gaussian (CG) models.
(1) Depending on the characters of medical images and advantages of Bayesian networks, it is proposed to use Bayesian networks to model medical image semantics. But classical Bayesian networks are adapt to discrete variables alone, and automated image features often are continuous. To use continuous ones in classical Bayesian networks, hybrid BN embedding CG is proposed. The CG makes a fuzzy discretization for continuous image features with probabilistic and uncertain nature. A semantic model with low-level image features alone is built, called BN-CG-Low. The experiment results show that this model can extract semantics from low-level image features, with good description of images.
(2) Based on BN-CG-Low, semantic model BN-CG fusing low-level image features and middle-level semantics is designed, to improve semantic precision and recall and describe image content better. It has better precision and recall, comparing with BN-CG-Low.
2. A three-level semantic model integrating Gaussian mixture models (GMM) into hybrid BN, named BN-GMM, is given.
In medical diagnosis, doctors give a conclusion after thinking over pathological objects from different views and on different levels, so a three- level medical images semantic model is proposed, in which GMM is used to capture middle-level object semantics, and then embedded into a Bayesian network. The experiment between BN-GMM and BN-KNN demonstrates that BN-GMM achieves better precision and recall, with better interpretation.
3. Hierarchical semantic similarity measure, based on distance in semantic probability space, is proposed.
Different level semantics has different weights in the semantic models, and semantic probability reflects semantic brief. It accords with the habit in medical diagnosis. Therefore, a hierarchical semantic similarity measure is studied, in which, semantic similarity measure depends on distance in its posterior probability space on every layer. The method is applied in semantic retrieval of astrocytoma malignant degree, and gets satisfying query performance.
4. A three-level semantic model integrating SVM into hybrid BN, named BN-SVM, is designed.
Lots of research proved SVM can get better precision than GMM, in case of small samples. So a three level medical images semantic model is designed, in which SVM are used to extract middle-level object semantics, and then embedded into a Bayesian network. From experiments between semantic model BN-SVM, BN-KNN-Low with KNN instead of SVM, BN-GMM-Low with GMM instead of SVM and BN-CG-Low with CG instead of SVM, BN-SVM gets better performance.
In case of the same samples in this paper, our new methods outperform the BN with KNN as object semantic detectors, when they are used in modeling medical image semantics.
The research of hierarchical knowledge expression, semantic extraction and semantic similarity measure provides a solution to enable medical image semantic retrieval by using variables keywords at different semantic levels.
本论文的主要研究成果及其创新点包括:
1、提出了引入条件高斯模型来模糊离散化连续变量的基于混合贝叶斯网络的医学图像语义建模的方法
(1)考虑到医学图像的特点以及贝叶斯网络的性能和优势,提出了利用贝叶斯网络来对医学图像的语义建模,但传统的贝叶斯网络只适用于离散变量,而自动提取的图像特征往往是连续的,为了可以在贝叶斯网络中使用连续变量,并考虑到医学图像特征的模糊性和不确定性,提出了使用条件高斯(conditional Gaussian, CG)模型对连续的视觉特征进行模糊离散化处理,然后嵌入到贝叶斯网络中,建立仅利用低层视觉特征的智能模型BN-CG-Low。仿真实验结果表明,该模型可以很好地描述图像的内容,从低层视觉特征自动提取高层语义,有效解决“语义鸿沟”问题,并提供了符合医学习惯的知识表达。
(2)在第1(1)点的基础上,考虑到贝叶斯网络的数据融合能力,为了更完整地描述图像内容和提高语义提取的准确率和查全率,提出了融合低层视觉特征和中层语义的语义模型BN-CG,通过与BN-CG-Low的比较实验,BN-CG可以进一步提高准确率和查全率。
2、给出了基于BN-GMM的医学图像的三层语义模型
医学图像的诊断中,医生的思维是着重病变区域的性质和特点,然后综合考虑从不同角度对病变区的理解和判断,最后得出病症语义。从这个特点出发,我们提出了首先利用高斯混合模型(Gaussian mixture models, GMM)对病变区域进行模糊识别,实现从视觉特征到对象语义的映射,然后利用贝叶斯网络融合各种从不同理解角度得到的对象语义,从而建立一个基于BN-GMM的医学图像三层语义模型。与使用K近邻分类器(KNN)代替GMM的BN-KNN相比,取得了更好的精度和语义的可解释性。
3、研究了分层的基于语义概率空间距离的语义相似度度量方法
在前面所提出的语义模型中,不同层次的语义其重要性是不一样的,语义的概率反映了语义的置信度,这也符合医学诊断的习惯,因此提出了按照语义层次的不同进行分层处理,在每一层分别通过度量语义的后验概率空间距离进行语义相似度的度量。将这种度量方法应用于医学图像的语义检索,取得了令人满意的查询结果。
4、设计了基于BN-SVM的医学图像三层语义模型
考虑到临床实际中取得大量具有病理结果的医学图像训练样本的困难,很多研究成果表明,在小样本的情况下,支持向量机(Support vector machine,SVM)能取得比GMM更高的识别精度,因此提出了首先利用支持向量机对病变区域进行识别,实现从低层视觉特征到对象语义的映射,然后利用贝叶斯网络融合各种从不同理解角度得到的对象语义(来源于不同的SVM),从而建立一个适用于小样本的基于BN-SVM的医学图像三层语义模型。实验结果表明,与采用K近邻分类器或GMM取代SVM的贝叶斯网络相比,取得了更好的结果。
在本文所提供的相同图像样本下,本文所提出的三种方法用于医学图象语义建模,均比以往的嵌入KNN的混合贝叶斯网络有着更高的准确率和查全率。
医学图像内容的分层和结构性表达、语义的自动获取、语义相似度的研究为实现能应用于医学临床的不同语义层次的检索提供了条件和基础。
The research of medical image semantic retrieval is a new hotspot in the field of medical image retrieval, and should be settled urgently in medicine, too. It is an interdisciplinary study on medical image understanding, denoting the synergy of medicine, image processing, pattern recognition, computer vision, machine learning, database and artificial intelligence. Semantic modeling and semantic similarity measure are its difficulty and key. The fundamental task of image semantic modeling is to extract implicit unknown high-level semantics in order to make up“semantic gap”. The dissertation tries to study medical image semantic modeling approach based on hybrid Bayesian networks (BN), thought about characters of medical images and demand in medicine. It includes the study of multi-level semantic statistical models of medical images, capture of object semantics and high-level semantics, and semantic similarity measure. To validate these methods, they are applied in the prediction of astrocytoma malignant degree, and semantic models of astrocytoma malignant degree and a semantic retrieval system are designed.
The main contributions of the dissertation are as follows:
1. It proposes a medical image semantic modeling method using hybrid BN embedding Conditional Gaussian (CG) models.
(1) Depending on the characters of medical images and advantages of Bayesian networks, it is proposed to use Bayesian networks to model medical image semantics. But classical Bayesian networks are adapt to discrete variables alone, and automated image features often are continuous. To use continuous ones in classical Bayesian networks, hybrid BN embedding CG is proposed. The CG makes a fuzzy discretization for continuous image features with probabilistic and uncertain nature. A semantic model with low-level image features alone is built, called BN-CG-Low. The experiment results show that this model can extract semantics from low-level image features, with good description of images.
(2) Based on BN-CG-Low, semantic model BN-CG fusing low-level image features and middle-level semantics is designed, to improve semantic precision and recall and describe image content better. It has better precision and recall, comparing with BN-CG-Low.
2. A three-level semantic model integrating Gaussian mixture models (GMM) into hybrid BN, named BN-GMM, is given.
In medical diagnosis, doctors give a conclusion after thinking over pathological objects from different views and on different levels, so a three- level medical images semantic model is proposed, in which GMM is used to capture middle-level object semantics, and then embedded into a Bayesian network. The experiment between BN-GMM and BN-KNN demonstrates that BN-GMM achieves better precision and recall, with better interpretation.
3. Hierarchical semantic similarity measure, based on distance in semantic probability space, is proposed.
Different level semantics has different weights in the semantic models, and semantic probability reflects semantic brief. It accords with the habit in medical diagnosis. Therefore, a hierarchical semantic similarity measure is studied, in which, semantic similarity measure depends on distance in its posterior probability space on every layer. The method is applied in semantic retrieval of astrocytoma malignant degree, and gets satisfying query performance.
4. A three-level semantic model integrating SVM into hybrid BN, named BN-SVM, is designed.
Lots of research proved SVM can get better precision than GMM, in case of small samples. So a three level medical images semantic model is designed, in which SVM are used to extract middle-level object semantics, and then embedded into a Bayesian network. From experiments between semantic model BN-SVM, BN-KNN-Low with KNN instead of SVM, BN-GMM-Low with GMM instead of SVM and BN-CG-Low with CG instead of SVM, BN-SVM gets better performance.
In case of the same samples in this paper, our new methods outperform the BN with KNN as object semantic detectors, when they are used in modeling medical image semantics.
The research of hierarchical knowledge expression, semantic extraction and semantic similarity measure provides a solution to enable medical image semantic retrieval by using variables keywords at different semantic levels.
引文
[1] Yanxi Liu, N. Lazar, W.E. Rothfus and F.Dellaert,“Semantic based Biomedical Image Indexing and Retrieval: Trends and Advances in Content-Based Image and Video Retrieval”[DB/OL], https://www.autonlab.org/autonweb/14673.html ,2004
[2] Lehmann T.M., Wein B.B., Keysers D., etal, “A monohierarchical multiaxial classification code for medical images in content-based retrieval”[C], Proceedings of 2002 IEEE International Symposium on Biomedical Imaging,2002: 313–316
[3] 王惠锋,孙正兴.“基于内容的图象检索中的语义处理方法.中国图象图形学报”[J]. 2001,6(10):945-952
[4] 李清勇,胡宏,施智平,史忠植. “基于纹理语义特征的图象检索研究”[J].计算机学报. 2006,29(1):116-123
[5] Naphade M.R., Huang, T.S.,“Extracting semantics from audio-visual content: the final frontier in multimedia retrieval”[J], Neural Networks, IEEE Transactions on, 2002,13(4): 793–810
[6] Yanxi Liu, Rothfus W E, Kanade T.,“Content-based 3D neuroradiologic image retrieval: preliminary results”[C],In Proceedings of 1998 IEEE International Workshop on Content-Based Access of Image and Video Database, 1998:91–100
[7] Mojsilovic A, Gomes J, “Semantic based categorization, browsing and retrieval in medical image databases” [C], In Proceedings of 2002 International Conference on Image Processing, 2002,3: 145-148
[8] Chu W W, Chih-Cheng Hsu, Cardenas A F, et al, “Knowledge-based image retrieval with spatial and temporal constructs” [J], IEEE Transactions on Knowledge and Data Engineering,1998,10(6): 872–888
[9] Naphade M.R.,Huang, T.S., “Inferring semantic concepts for video indexing and retrieval” [C], In Proceedings of International Conference on Image Processing, 2000, Vol.3: 766-769
[10] Naphade M.R., Basu, S., Smith, J.R., Ching-Yung Lin, Tseng, B., “Modeling semantic concepts to support query by keywords in video” [C], In Proceedings of 2002 International Conference on Image Processing, 2002,Vol.1: 145-148
[11] Naphade M.R. , Ching-Yung Lin , Smith,J.R., “ Learning semantic multimedia representations from a small set of examples” [C], In Proceedings of 2002 IEEEInternational Conference on Multimedia and Expo, 2002, Vol.2:513-516
[12] Naphade M.R., Ching-Yung Lin, Natsev, A., Tseng, B.L., Smith, J.R., “A framework for moderate vocabulary semantic visual concept detection” [C], In Proceedings of 2003 International Conference on Multimedia and Expo, 2003, Vol.1: 437-440
[13] Naphade M.R., Smith, J.R., “Learning visual models of semantic concepts” [C], In Proceedings of 2003 International Conference on Image Processing, 2003,Vol.2: 531-534
[14] 马军,杨杰,耿道颖. “基于贝叶斯网络的脑胶质瘤恶性高低度的自动诊断” [J]。生物医学工程学杂志. 2006,23(1):184-188
[15] Kane M.J., Savakis A., “Bayesian network structure learning and inference in indoor vs. outdoor image classification” [C], In Proceedings of the 17th International Conference on Pattern Recognition,2004, Vol.2: 479–482
[16] Changbo Yang, Ming Dong, Fotouhi F., “Image content annotation using Bayesian framework and complement components analysis” [C], Image Processing, IEEE International Conference on, 2005,Vol.1: 1193-1196
[17] Ruofei Zhang, Zhongfei Zhang, Mingjing Li, Wei-Ying Ma, Hong-Jiang Zhang, “A probabilistic semantic model for image annotation and multimodal image retrieval” [C], In Proceedings of the tenth IEEE International Conference on Computer Vision, 2005, Vol.1: 846-851
[18] Naphade M.R., “On supervision and statistical learning for semantic multimedia analysis” [J], J.Vis.Commun.Image R.15(2004): 348-369
[19] Ramesh Naphade, M., Kozintsev, I.V., Huang, T.S., “Factor graph framework for semantic video indexing” [J],Circuits and Systems for Video Technology, IEEE Transactions on, 2002, Vol.12(1): 40–52
[20] Naphade M.R., Basu, S., Smith, J.R., Ching-Yung Lin, Tseng, B., “Modeling semantic concepts to support query by keywords in video” [C], In Proceedings of 2002 International Conference on Image Processing, 2002,Vol.1: 145-148
[21] 姜兴岳,耿道颖,沈天真等. “人工神经元网络鉴别星形胶质细胞瘤良恶性的初步研究[J]”.中国医学计算机成像杂志. 2004,10(4):217-220.
[22] Naphade M.R., Ching-Yung Lin, Natsev A.,Tseng B.L., Smith J.R., “A framework for moderate vocabulary semantic visual concept detection” [C], In proceedings of 2003 International Conference on Multimedia and Expo,2003, Vol.1: 437-440
[23] Naphade M.R., Smith J.R., “Learning visual models of semantic concepts” [C], In Proceedings of 2003 International Conference on Image Processing, 2003,Vol.2: 531-534
[24] 孙志杰,许宏丽. “一种图象底层视觉特征到高层语义的映射方法” [J].计算机应用. 2004,24(12):22-24
[25] Yutao Han, Xiaojun Qi, “A complementary SVMs-based image annotation system” [C], IEEE International Conference on Image Processing, 2005,Vol.1: 1185-1188
[26] Changbo Yang, Ming Dong, Jing Hua,“Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning” [C], IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006,Vol.2: 2057-2063
[27] Qing Yang, Fang-Min Li, “Support vector machine for customized email filtering based on improving latent semantic indexing” [C], In Proceedings of 2005 International Conference on Machine Learning and Cybernetics, 2006, Vol.6: 3787- 3791
[28] Yuli Gao, Jianping Fan, “Semantic Image Classification with Hierarchical Feature Subset Selection” [C], MIR’05, 2005,Singapore: 135-142
[29] Jiebo Luo, Andreas E. Savakis and Amit Singhal, “A Bayesian network-based framework for semantic image understanding” [J], Pattern Recognition, 2005,38(6): 919-934
[30] Fan J.,Gao Y.,and Luo H., “Multi-level annotation of natural scenes using dominant image components and semantic concepts” [C], In proceedings of the ACM International Conference on Multimedia. ACM, 2004, New York: 540-547
[31] 黄伟,戴蓓倩,李辉. “基于分类特征空间高斯混合模型和神经网络融合的说话人识别” [J].电子与信息学报. 2004,26(10):1607-1612
[32] Igor Kononenko, “Machine learning for medical diagnosis:history, state of the art and perspective” [J], Artificial Intelligence in Medicine. 2001,23(1): 89-109
[33] Susan L.Mabry, Caleb R.Hug, Russell C.Roundy, “Clinical Decision Support with IM_Agents and ERMA Multi-agents” [C], Proceeding of the 17th IEEE Symposium on Computer_based Medical Systems, 2004
[34] Daniel Nikovski, “Constructing Bayesian Networks for Medical Diagnosis from Incomplete and Partially Correct Statistics” [J], IEEE transaction on Knowledge and Data Engineering, 2000,12(4): 509-516
[35] 王莉莉,杨杰. “基于医学图象形状分析的脑肿瘤自动辅助诊断系统” [J].中国医疗器械杂志. 2005,29(2):87-91
[36] Mufit Ferman A., Murat Tekalp A., “Probabilistic analysis and extraction of video content” [C], In Proceedings of 1999 International Conference on Image Processing, 1999, Vol.2: 91 - 95
[37] Luo Y.,Hwang J.N.,“Video sequence modeling by dynamic Bayesian networks: a systematic approach from coarse-to-fine grains” [C], In Proceedings of 2003 International Conference on Image Processing, 2003, Vol.2: 615-618
[38] Theocharous G., Rohanimanesh K., Maharlevan S., “Learning hierarchical observable Markov decision process models for robot navigation” [C], In Proceedings of IEEE International Conference on Robotics and Automation, 2001, Vol.1: 511-516
[39] Luhr S., Bui H.H., Venkatesh S., West G.A.W., “Recognition of human activity through hierarchical stochastic learning” [C], In Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003: 416–422
[40] Lam S.W., Hui W. K., “Reading constrained text using hierarchical hidden Markov models” [C], In Proceedings of the Second International Conference on Document Analysis and Recognition, 1993: 151–154
[41] Nel, P., du Preez, J., “Automatic syllabification using hierarchical hidden Markov models” [C], In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003, Vol.1: 768-771
[42] Noda I., “Hierarchical hidden Markov modeling for team-play in multiple agents” [C], IEEE International Conference on Systems, Man and Cybernetics, 2003, Vol.1: 38–45
[43] Theocharous G., Mahadevan S., “Learning the hierarchical structure of spatial environments using multiresolution statistical models” [C], IEEE/RSJ International Conference on Intelligent Robots and System, 2002, Vol.1: 1038-1043
[44] Lexing Xie, Shih-Fu Chang, Divakaran A., Huifang Sun, “Unsupervised discovery of multilevel statistical video structures using hierarchical hidden Markov models” [C], Proceedings of 2003 International Conference on Multimedia and Expo, 2003,Vol.3: 29-32
[45] Rea N., Dahyot R., Kokaram A., “Modeling high level structure in sports with motion driven HMMs” [C], In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004, Vol.3: 621-624
[46] Jaser E., Kittler J., Christmas W., “Hierarchical decision making scheme for sports video categorisation with temporal post-processing” [C], In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, Vol.2: 908-913
[47] Tadeusiewicz R.,Marek R.Ogiela, “Artificial Intelligence Techniques in Retrieval of Visual Data Semantic Information” [C], AWIC 2003,LNAI 2663.18-27
[48] LiuWenyin, HongJiang Zhang, “Semi-automatic Image Annotation” [C], IEEE Int. Conf. Image processing, 2003
[49] Petrakis E.G.M., Faloutsos,“A Similarity searching in medical image databases” [J], IEEE Transactions on Knowledge and Data Engineering, 1997,9(3): 435–447
[50] Hongshun Su, Wei Qian, Sankar R,et al, “A new knowledge-based lung nodule detection system” [C], In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004, Vol.5: 445-448
[51] Chbeir R., Favetta F., “A global description of medical imaging with high precision” [J], IEEE Transactions on Systems, Man and Cybernetics, Part B, 2003,33(5): 752–757
[52] Zrimec T, “A content-based retrieval system for medical images. Control” [C], 7th International Conference on Automation, Robotics and Vision, 2002,1: 180–185
[53] Lehmann T.M, Wein B B, Keysers D,etal, “A monohierarchical multiaxial classification code for medical images in content-based retrieval” [C], Proceedings of 2002 IEEE International Symposium on Biomedical Imaging ,2002: 313–316
[54] Chbeir R, Amghar Y., Flory A, et al, “A hyper-spaced data model for content and semantic-based medical image retrieval” [C], ACS/IEEE International Conference on Computer Systems and Applications, 2001: 161–167
[55] Chbeir R., Atnafu S., Brunie L., “Image data model for an efficient multi-criteria query: a case in medical databases” [C], In Proceedings of 14th International Conference on Scientific and Statistical Database Management, 2002: 165-174
[56] Henry J Lowe MD, Ilya Antipov BS, William Hersh MD, et al ,“ Towards Knowledge-Based Retrieval of Medical Images. The Role of Semantic Indexing, Image Content Representation and Knowledge-Based Retrieval” [C], In American Medical Informatics Association Annual Symposium (AMIA), 1998
[57] 邵虹,崔文成,张继武. “低级特征和语义特征相结合的医学图像检索方法” [J].中国图像图形学报. 2004,9(2):220-223
[58] Daniel keysers, Berthold B.Wein, T. Lehmann, “Statistical framework for model-based image retrieval in medical applications” [J], Journal of Electronic Imageing, 2003,12(1): 59-68
[59] T.M.Lehmann,Henning Schubert,Daniel Keysers,et al,“The IRMA code for unique classification of medical images” [C], Proceedings of the SPIE Conference on Medicalimaging,vol.5033,San Diego, CA, USA, 2003
[60] 周杰. “基于内容的医学图像检索” [D].第一军医大学博士论文,2004
[61] Chang E., Kingshy Goh, Sychay G., Gang Wu, “CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines” [J], IEEE Transactions on Circuits and Systems for Video Technology, 2003,13(1): 26 -38
[62] Mojsilovic A.,Gomes J.,“Semantic based categorization, browsing and retrieval in medical image databases” [C], Proceedings of 2002 International Conference on Image Processing,2002, 3: 145-148
[63] Ruofei Zhang,Zhongfei Zhang, “Hidden semantic concept discovery in region based image retrieval” [C],In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, Vol.2: 996-1001
[64] 陈久军. “基于统计学习的图像语义挖掘研究” [D].浙江大学博士学位论文.2006
[65] 代科学,付畅俭, 武德峰,李国辉.“视频挖掘:概念、技术与应用” [J]. 计算机应用研究. 2006 年 01 期 :1-4
[66] Xin-Jing Wang, Lei Zhang and Feng Jing, et al, “AnnoSearch: Image Auto-Annotation by Search” [C], In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2006
[67] Yumei Miao, Yimin Wang, Yusong Miao, “The research of semantic content applied to brain CT images” [C], Processings of the 17th IEEE Symposium on Computer-Based Medical Systems, 2004
[68] JunWei Han, Lei Guo, “A New Image Retrieval System Supporting Query by Semantics and Example” [C],2002,IEEE:953-955
[69] 章毓晋. “基于内容的视觉信息检索” [M].科学出版社,北京.2003:444-456
[70] 程 亚 娟 . “ 基 于 区 域 语 义 和 低 层 特 征 的 图 像 关 键 技 术 研 究 ” [J]. http://e44.cnki.net/kns50/, 2004
[71] 沈玉利,任建峰等. “一种由低层视觉特征获取高层语义的图像检索方法” [J].计算机工程. 2005,31(1): 172-178
[72] Hongshun Su, Wei Qian, Sankar R.,Xuejun Sun,“A new knowledge-based lung nodule detection system” [C], In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004, Vol.5: 445-448
[73] Zoé Lacroix, Louiqa Raschid and María Esther Vidal, “Semantic Model to IntegrateBiological Resources” [C], In Proceedings of the 22nd International Conference on Data Engineering Workshops 2006
[74] Zrimec T., “A content-based retrieval system for medical images” [C], 7th International Conference on Control, Automation, Robotics and Vision, 2002,Vol.1: 180–185
[75] Chbeir R., Amghar Y., Flory A., etal, “A hyper-spaced data model for content and semantic-based medical image retrieval” [C], ACS/IEEE International Conference on Computer Systems and Applications, 2001: 161–167
[76] Atnafu S., Chbeir R., Brunie L., “Content-based and metadata retrieval in medical image database” [C], In Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems, 2002: 327–332
[77] Chbeir R., Favetta F., “A Global Description of Medical Image with High Precision” [C], In Proceedings of the IEEE International Symposium on Bio-Informatics and Biomedical Engineering (BIBE'00), 2000: 289-294
[78] Chbeir R., Atnafu S., Brunie L., “Image Data Model for Efficient Multi-Criteria Query in Medical Database” [C], 14th International Conference on Scientific and Statistical Database Management, IEEE Computer Society, 2002, Edinburgh, Scotland
[79] T. Lehmann, B. Wein, J. Dahmen, etal. “Content-Based Image Retrieval in Medical Applications: A Novel Multi-Step Approach[J]”, SPIE_2000,3972: 312-320
[80] T.M.Lehmann, M.O.Güld, C.Thies, et al,“Content-based image retrieval in medical applications for picture archiving and communication system” [C],Proceedings of the SPIE Conference on Medical imaging,2003,Vol.5033
[81] Chbeir R, Amghar Y., Flory A, et al. “A hyper-spaced data model for content and semantic-based medical image retrieval” [C]. ACS/IEEE International Conference on Computer Systems and Applications, 2001: 161–167
[82] Chbeir R., Atnafu S., Brunie L., “Image data model for an efficient multi-criteria query: a case in medical databases” [C], In Proceedings of 14th International Conference on Scientific and Statistical Database Management, 2002: 165-174
[83] Chbeir R., Favetta F., “A global description of medical imaging with high precision” [J], Systems, Man and Cybernetics,Part B,IEEE Transactions on , 2003,33(5): 752–757
[84] Jian Yao, Sameer Antani and Rodney Long, “Automatic Medical Image Annotation and Retrieval Using SECC” [C], In Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems 2006
[85] Tang L.H.,Hanka R., Ip H.H.S., “An intelligent system for integrating semantic and iconic features for image retrieval” [C], In Proceedings of Computer Graphics International 2001: 240–246
[86] 刘定西,于群. “MR 成像分册[M]”. 湖北科学技术出版社,武汉.2000
[87] 张雪林. “医学影像学[M]”.人民卫生出版社.2001:59
[88] 边肇祺,张学工等. “模式识别[M]”. 清华大学出版社,北京,2000:284-303
[89] Vapnik V., “The nature of statistical learning theory[M]”, Spring-Verlag New YorkInc., 1995,张学工译. 统计学习理论的本质. 北京:清华大学出版社,1999
[90] 邓乃扬,田英杰. “数据挖掘中的新方法—支持向量机[M]”. 科学出版社. 2004.
[91] Nello Cristianini, “Support Vector Machines[M]”, China Machine Press, 2005
[92] Finn V. Jensen, “Bayesian networks and decision graphs[M]”, New York: Springer, 2001
[93] David Heckerman, “A tutorial on learning with Bayesian network[R]”, Technical Report MSD-TR-95-06, Microsoft Research, March 1995
[94] Kim J H, Pearl J. , “A computational model for causal and diagnostic reasoning in inference system” [C], In Proc the 8th Intenational Joint Conference on Artificial Intelligence, 1983,190-193
[95] Stuart Russell, Peter Norving. “人工智能[M]”. 姜哲,金奕和张敏等译. 人民邮电出版社. 2004
[96] Tom M. Mitchell, “机器学习[M]”, 曾华军等译. 机械工业出版社, 2003:112-115
[97] Apte Chidan, Weiss Sholom, “ Data mining with decision trees and decision rules[J]”, Future Generation Computer Systems, 1997,13: 197-210
[98] YE Chen-zhou, YANG Jie, GENG Dao-ying, et al. “Fuzzy Rules to Predict Degree of Malignancy in Brain Glioma”[D],http://www.paper.edu.cn /schoolar/down-load.jsp. 2005: 1-29
[99] Hofmann R, Tresp V., “Discovering structures in continuous variables using Bayesian networks” [J], Advances in Neural Information Processing systems. 1996:500-506
[100] Monti S., Cooper G. F., “Learning Bayesian belief networks with neural network estimators” [C], In proceedings of the 16th Annual Conference network estimators, 1996: 579-584
[101] 聂生东,章鲁,顾顺德等. “磁共振图像的分割[J]”. 国外医学生物医学工程分册,1999,22(6):350
[102] 章毓晋. “图象处理与分析[M]”. 第 2 版. 清华大学出版社,2006
[103] Heckerman D.,“Bayesian networks for data Mining” [R], Data Mining and knowledge Discovery, 1997: 1-79
[104] Jeff A.Bilmes,“ A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models” [R], International Computer Science Institute,1998: 1-13.
[105] Qing Yang, Fang-Min Li, “Support Vector Machine for Customized Email Filtring Based on Improving Latent Semantic Indexing” [C], In Proceedings of Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 2005: 3787-3791
[2] Lehmann T.M., Wein B.B., Keysers D., etal, “A monohierarchical multiaxial classification code for medical images in content-based retrieval”[C], Proceedings of 2002 IEEE International Symposium on Biomedical Imaging,2002: 313–316
[3] 王惠锋,孙正兴.“基于内容的图象检索中的语义处理方法.中国图象图形学报”[J]. 2001,6(10):945-952
[4] 李清勇,胡宏,施智平,史忠植. “基于纹理语义特征的图象检索研究”[J].计算机学报. 2006,29(1):116-123
[5] Naphade M.R., Huang, T.S.,“Extracting semantics from audio-visual content: the final frontier in multimedia retrieval”[J], Neural Networks, IEEE Transactions on, 2002,13(4): 793–810
[6] Yanxi Liu, Rothfus W E, Kanade T.,“Content-based 3D neuroradiologic image retrieval: preliminary results”[C],In Proceedings of 1998 IEEE International Workshop on Content-Based Access of Image and Video Database, 1998:91–100
[7] Mojsilovic A, Gomes J, “Semantic based categorization, browsing and retrieval in medical image databases” [C], In Proceedings of 2002 International Conference on Image Processing, 2002,3: 145-148
[8] Chu W W, Chih-Cheng Hsu, Cardenas A F, et al, “Knowledge-based image retrieval with spatial and temporal constructs” [J], IEEE Transactions on Knowledge and Data Engineering,1998,10(6): 872–888
[9] Naphade M.R.,Huang, T.S., “Inferring semantic concepts for video indexing and retrieval” [C], In Proceedings of International Conference on Image Processing, 2000, Vol.3: 766-769
[10] Naphade M.R., Basu, S., Smith, J.R., Ching-Yung Lin, Tseng, B., “Modeling semantic concepts to support query by keywords in video” [C], In Proceedings of 2002 International Conference on Image Processing, 2002,Vol.1: 145-148
[11] Naphade M.R. , Ching-Yung Lin , Smith,J.R., “ Learning semantic multimedia representations from a small set of examples” [C], In Proceedings of 2002 IEEEInternational Conference on Multimedia and Expo, 2002, Vol.2:513-516
[12] Naphade M.R., Ching-Yung Lin, Natsev, A., Tseng, B.L., Smith, J.R., “A framework for moderate vocabulary semantic visual concept detection” [C], In Proceedings of 2003 International Conference on Multimedia and Expo, 2003, Vol.1: 437-440
[13] Naphade M.R., Smith, J.R., “Learning visual models of semantic concepts” [C], In Proceedings of 2003 International Conference on Image Processing, 2003,Vol.2: 531-534
[14] 马军,杨杰,耿道颖. “基于贝叶斯网络的脑胶质瘤恶性高低度的自动诊断” [J]。生物医学工程学杂志. 2006,23(1):184-188
[15] Kane M.J., Savakis A., “Bayesian network structure learning and inference in indoor vs. outdoor image classification” [C], In Proceedings of the 17th International Conference on Pattern Recognition,2004, Vol.2: 479–482
[16] Changbo Yang, Ming Dong, Fotouhi F., “Image content annotation using Bayesian framework and complement components analysis” [C], Image Processing, IEEE International Conference on, 2005,Vol.1: 1193-1196
[17] Ruofei Zhang, Zhongfei Zhang, Mingjing Li, Wei-Ying Ma, Hong-Jiang Zhang, “A probabilistic semantic model for image annotation and multimodal image retrieval” [C], In Proceedings of the tenth IEEE International Conference on Computer Vision, 2005, Vol.1: 846-851
[18] Naphade M.R., “On supervision and statistical learning for semantic multimedia analysis” [J], J.Vis.Commun.Image R.15(2004): 348-369
[19] Ramesh Naphade, M., Kozintsev, I.V., Huang, T.S., “Factor graph framework for semantic video indexing” [J],Circuits and Systems for Video Technology, IEEE Transactions on, 2002, Vol.12(1): 40–52
[20] Naphade M.R., Basu, S., Smith, J.R., Ching-Yung Lin, Tseng, B., “Modeling semantic concepts to support query by keywords in video” [C], In Proceedings of 2002 International Conference on Image Processing, 2002,Vol.1: 145-148
[21] 姜兴岳,耿道颖,沈天真等. “人工神经元网络鉴别星形胶质细胞瘤良恶性的初步研究[J]”.中国医学计算机成像杂志. 2004,10(4):217-220.
[22] Naphade M.R., Ching-Yung Lin, Natsev A.,Tseng B.L., Smith J.R., “A framework for moderate vocabulary semantic visual concept detection” [C], In proceedings of 2003 International Conference on Multimedia and Expo,2003, Vol.1: 437-440
[23] Naphade M.R., Smith J.R., “Learning visual models of semantic concepts” [C], In Proceedings of 2003 International Conference on Image Processing, 2003,Vol.2: 531-534
[24] 孙志杰,许宏丽. “一种图象底层视觉特征到高层语义的映射方法” [J].计算机应用. 2004,24(12):22-24
[25] Yutao Han, Xiaojun Qi, “A complementary SVMs-based image annotation system” [C], IEEE International Conference on Image Processing, 2005,Vol.1: 1185-1188
[26] Changbo Yang, Ming Dong, Jing Hua,“Region-based Image Annotation using Asymmetrical Support Vector Machine-based Multiple-Instance Learning” [C], IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2006,Vol.2: 2057-2063
[27] Qing Yang, Fang-Min Li, “Support vector machine for customized email filtering based on improving latent semantic indexing” [C], In Proceedings of 2005 International Conference on Machine Learning and Cybernetics, 2006, Vol.6: 3787- 3791
[28] Yuli Gao, Jianping Fan, “Semantic Image Classification with Hierarchical Feature Subset Selection” [C], MIR’05, 2005,Singapore: 135-142
[29] Jiebo Luo, Andreas E. Savakis and Amit Singhal, “A Bayesian network-based framework for semantic image understanding” [J], Pattern Recognition, 2005,38(6): 919-934
[30] Fan J.,Gao Y.,and Luo H., “Multi-level annotation of natural scenes using dominant image components and semantic concepts” [C], In proceedings of the ACM International Conference on Multimedia. ACM, 2004, New York: 540-547
[31] 黄伟,戴蓓倩,李辉. “基于分类特征空间高斯混合模型和神经网络融合的说话人识别” [J].电子与信息学报. 2004,26(10):1607-1612
[32] Igor Kononenko, “Machine learning for medical diagnosis:history, state of the art and perspective” [J], Artificial Intelligence in Medicine. 2001,23(1): 89-109
[33] Susan L.Mabry, Caleb R.Hug, Russell C.Roundy, “Clinical Decision Support with IM_Agents and ERMA Multi-agents” [C], Proceeding of the 17th IEEE Symposium on Computer_based Medical Systems, 2004
[34] Daniel Nikovski, “Constructing Bayesian Networks for Medical Diagnosis from Incomplete and Partially Correct Statistics” [J], IEEE transaction on Knowledge and Data Engineering, 2000,12(4): 509-516
[35] 王莉莉,杨杰. “基于医学图象形状分析的脑肿瘤自动辅助诊断系统” [J].中国医疗器械杂志. 2005,29(2):87-91
[36] Mufit Ferman A., Murat Tekalp A., “Probabilistic analysis and extraction of video content” [C], In Proceedings of 1999 International Conference on Image Processing, 1999, Vol.2: 91 - 95
[37] Luo Y.,Hwang J.N.,“Video sequence modeling by dynamic Bayesian networks: a systematic approach from coarse-to-fine grains” [C], In Proceedings of 2003 International Conference on Image Processing, 2003, Vol.2: 615-618
[38] Theocharous G., Rohanimanesh K., Maharlevan S., “Learning hierarchical observable Markov decision process models for robot navigation” [C], In Proceedings of IEEE International Conference on Robotics and Automation, 2001, Vol.1: 511-516
[39] Luhr S., Bui H.H., Venkatesh S., West G.A.W., “Recognition of human activity through hierarchical stochastic learning” [C], In Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003: 416–422
[40] Lam S.W., Hui W. K., “Reading constrained text using hierarchical hidden Markov models” [C], In Proceedings of the Second International Conference on Document Analysis and Recognition, 1993: 151–154
[41] Nel, P., du Preez, J., “Automatic syllabification using hierarchical hidden Markov models” [C], In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003, Vol.1: 768-771
[42] Noda I., “Hierarchical hidden Markov modeling for team-play in multiple agents” [C], IEEE International Conference on Systems, Man and Cybernetics, 2003, Vol.1: 38–45
[43] Theocharous G., Mahadevan S., “Learning the hierarchical structure of spatial environments using multiresolution statistical models” [C], IEEE/RSJ International Conference on Intelligent Robots and System, 2002, Vol.1: 1038-1043
[44] Lexing Xie, Shih-Fu Chang, Divakaran A., Huifang Sun, “Unsupervised discovery of multilevel statistical video structures using hierarchical hidden Markov models” [C], Proceedings of 2003 International Conference on Multimedia and Expo, 2003,Vol.3: 29-32
[45] Rea N., Dahyot R., Kokaram A., “Modeling high level structure in sports with motion driven HMMs” [C], In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004, Vol.3: 621-624
[46] Jaser E., Kittler J., Christmas W., “Hierarchical decision making scheme for sports video categorisation with temporal post-processing” [C], In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, Vol.2: 908-913
[47] Tadeusiewicz R.,Marek R.Ogiela, “Artificial Intelligence Techniques in Retrieval of Visual Data Semantic Information” [C], AWIC 2003,LNAI 2663.18-27
[48] LiuWenyin, HongJiang Zhang, “Semi-automatic Image Annotation” [C], IEEE Int. Conf. Image processing, 2003
[49] Petrakis E.G.M., Faloutsos,“A Similarity searching in medical image databases” [J], IEEE Transactions on Knowledge and Data Engineering, 1997,9(3): 435–447
[50] Hongshun Su, Wei Qian, Sankar R,et al, “A new knowledge-based lung nodule detection system” [C], In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004, Vol.5: 445-448
[51] Chbeir R., Favetta F., “A global description of medical imaging with high precision” [J], IEEE Transactions on Systems, Man and Cybernetics, Part B, 2003,33(5): 752–757
[52] Zrimec T, “A content-based retrieval system for medical images. Control” [C], 7th International Conference on Automation, Robotics and Vision, 2002,1: 180–185
[53] Lehmann T.M, Wein B B, Keysers D,etal, “A monohierarchical multiaxial classification code for medical images in content-based retrieval” [C], Proceedings of 2002 IEEE International Symposium on Biomedical Imaging ,2002: 313–316
[54] Chbeir R, Amghar Y., Flory A, et al, “A hyper-spaced data model for content and semantic-based medical image retrieval” [C], ACS/IEEE International Conference on Computer Systems and Applications, 2001: 161–167
[55] Chbeir R., Atnafu S., Brunie L., “Image data model for an efficient multi-criteria query: a case in medical databases” [C], In Proceedings of 14th International Conference on Scientific and Statistical Database Management, 2002: 165-174
[56] Henry J Lowe MD, Ilya Antipov BS, William Hersh MD, et al ,“ Towards Knowledge-Based Retrieval of Medical Images. The Role of Semantic Indexing, Image Content Representation and Knowledge-Based Retrieval” [C], In American Medical Informatics Association Annual Symposium (AMIA), 1998
[57] 邵虹,崔文成,张继武. “低级特征和语义特征相结合的医学图像检索方法” [J].中国图像图形学报. 2004,9(2):220-223
[58] Daniel keysers, Berthold B.Wein, T. Lehmann, “Statistical framework for model-based image retrieval in medical applications” [J], Journal of Electronic Imageing, 2003,12(1): 59-68
[59] T.M.Lehmann,Henning Schubert,Daniel Keysers,et al,“The IRMA code for unique classification of medical images” [C], Proceedings of the SPIE Conference on Medicalimaging,vol.5033,San Diego, CA, USA, 2003
[60] 周杰. “基于内容的医学图像检索” [D].第一军医大学博士论文,2004
[61] Chang E., Kingshy Goh, Sychay G., Gang Wu, “CBSA: content-based soft annotation for multimodal image retrieval using Bayes point machines” [J], IEEE Transactions on Circuits and Systems for Video Technology, 2003,13(1): 26 -38
[62] Mojsilovic A.,Gomes J.,“Semantic based categorization, browsing and retrieval in medical image databases” [C], Proceedings of 2002 International Conference on Image Processing,2002, 3: 145-148
[63] Ruofei Zhang,Zhongfei Zhang, “Hidden semantic concept discovery in region based image retrieval” [C],In Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004, Vol.2: 996-1001
[64] 陈久军. “基于统计学习的图像语义挖掘研究” [D].浙江大学博士学位论文.2006
[65] 代科学,付畅俭, 武德峰,李国辉.“视频挖掘:概念、技术与应用” [J]. 计算机应用研究. 2006 年 01 期 :1-4
[66] Xin-Jing Wang, Lei Zhang and Feng Jing, et al, “AnnoSearch: Image Auto-Annotation by Search” [C], In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2006
[67] Yumei Miao, Yimin Wang, Yusong Miao, “The research of semantic content applied to brain CT images” [C], Processings of the 17th IEEE Symposium on Computer-Based Medical Systems, 2004
[68] JunWei Han, Lei Guo, “A New Image Retrieval System Supporting Query by Semantics and Example” [C],2002,IEEE:953-955
[69] 章毓晋. “基于内容的视觉信息检索” [M].科学出版社,北京.2003:444-456
[70] 程 亚 娟 . “ 基 于 区 域 语 义 和 低 层 特 征 的 图 像 关 键 技 术 研 究 ” [J]. http://e44.cnki.net/kns50/, 2004
[71] 沈玉利,任建峰等. “一种由低层视觉特征获取高层语义的图像检索方法” [J].计算机工程. 2005,31(1): 172-178
[72] Hongshun Su, Wei Qian, Sankar R.,Xuejun Sun,“A new knowledge-based lung nodule detection system” [C], In Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing, 2004, Vol.5: 445-448
[73] Zoé Lacroix, Louiqa Raschid and María Esther Vidal, “Semantic Model to IntegrateBiological Resources” [C], In Proceedings of the 22nd International Conference on Data Engineering Workshops 2006
[74] Zrimec T., “A content-based retrieval system for medical images” [C], 7th International Conference on Control, Automation, Robotics and Vision, 2002,Vol.1: 180–185
[75] Chbeir R., Amghar Y., Flory A., etal, “A hyper-spaced data model for content and semantic-based medical image retrieval” [C], ACS/IEEE International Conference on Computer Systems and Applications, 2001: 161–167
[76] Atnafu S., Chbeir R., Brunie L., “Content-based and metadata retrieval in medical image database” [C], In Proceedings of the 15th IEEE Symposium on Computer-Based Medical Systems, 2002: 327–332
[77] Chbeir R., Favetta F., “A Global Description of Medical Image with High Precision” [C], In Proceedings of the IEEE International Symposium on Bio-Informatics and Biomedical Engineering (BIBE'00), 2000: 289-294
[78] Chbeir R., Atnafu S., Brunie L., “Image Data Model for Efficient Multi-Criteria Query in Medical Database” [C], 14th International Conference on Scientific and Statistical Database Management, IEEE Computer Society, 2002, Edinburgh, Scotland
[79] T. Lehmann, B. Wein, J. Dahmen, etal. “Content-Based Image Retrieval in Medical Applications: A Novel Multi-Step Approach[J]”, SPIE_2000,3972: 312-320
[80] T.M.Lehmann, M.O.Güld, C.Thies, et al,“Content-based image retrieval in medical applications for picture archiving and communication system” [C],Proceedings of the SPIE Conference on Medical imaging,2003,Vol.5033
[81] Chbeir R, Amghar Y., Flory A, et al. “A hyper-spaced data model for content and semantic-based medical image retrieval” [C]. ACS/IEEE International Conference on Computer Systems and Applications, 2001: 161–167
[82] Chbeir R., Atnafu S., Brunie L., “Image data model for an efficient multi-criteria query: a case in medical databases” [C], In Proceedings of 14th International Conference on Scientific and Statistical Database Management, 2002: 165-174
[83] Chbeir R., Favetta F., “A global description of medical imaging with high precision” [J], Systems, Man and Cybernetics,Part B,IEEE Transactions on , 2003,33(5): 752–757
[84] Jian Yao, Sameer Antani and Rodney Long, “Automatic Medical Image Annotation and Retrieval Using SECC” [C], In Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems 2006
[85] Tang L.H.,Hanka R., Ip H.H.S., “An intelligent system for integrating semantic and iconic features for image retrieval” [C], In Proceedings of Computer Graphics International 2001: 240–246
[86] 刘定西,于群. “MR 成像分册[M]”. 湖北科学技术出版社,武汉.2000
[87] 张雪林. “医学影像学[M]”.人民卫生出版社.2001:59
[88] 边肇祺,张学工等. “模式识别[M]”. 清华大学出版社,北京,2000:284-303
[89] Vapnik V., “The nature of statistical learning theory[M]”, Spring-Verlag New YorkInc., 1995,张学工译. 统计学习理论的本质. 北京:清华大学出版社,1999
[90] 邓乃扬,田英杰. “数据挖掘中的新方法—支持向量机[M]”. 科学出版社. 2004.
[91] Nello Cristianini, “Support Vector Machines[M]”, China Machine Press, 2005
[92] Finn V. Jensen, “Bayesian networks and decision graphs[M]”, New York: Springer, 2001
[93] David Heckerman, “A tutorial on learning with Bayesian network[R]”, Technical Report MSD-TR-95-06, Microsoft Research, March 1995
[94] Kim J H, Pearl J. , “A computational model for causal and diagnostic reasoning in inference system” [C], In Proc the 8th Intenational Joint Conference on Artificial Intelligence, 1983,190-193
[95] Stuart Russell, Peter Norving. “人工智能[M]”. 姜哲,金奕和张敏等译. 人民邮电出版社. 2004
[96] Tom M. Mitchell, “机器学习[M]”, 曾华军等译. 机械工业出版社, 2003:112-115
[97] Apte Chidan, Weiss Sholom, “ Data mining with decision trees and decision rules[J]”, Future Generation Computer Systems, 1997,13: 197-210
[98] YE Chen-zhou, YANG Jie, GENG Dao-ying, et al. “Fuzzy Rules to Predict Degree of Malignancy in Brain Glioma”[D],http://www.paper.edu.cn /schoolar/down-load.jsp. 2005: 1-29
[99] Hofmann R, Tresp V., “Discovering structures in continuous variables using Bayesian networks” [J], Advances in Neural Information Processing systems. 1996:500-506
[100] Monti S., Cooper G. F., “Learning Bayesian belief networks with neural network estimators” [C], In proceedings of the 16th Annual Conference network estimators, 1996: 579-584
[101] 聂生东,章鲁,顾顺德等. “磁共振图像的分割[J]”. 国外医学生物医学工程分册,1999,22(6):350
[102] 章毓晋. “图象处理与分析[M]”. 第 2 版. 清华大学出版社,2006
[103] Heckerman D.,“Bayesian networks for data Mining” [R], Data Mining and knowledge Discovery, 1997: 1-79
[104] Jeff A.Bilmes,“ A Gentle Tutorial of the EM Algorithm and its Application to Parameter Estimation for Gaussian Mixture and Hidden Markov Models” [R], International Computer Science Institute,1998: 1-13.
[105] Qing Yang, Fang-Min Li, “Support Vector Machine for Customized Email Filtring Based on Improving Latent Semantic Indexing” [C], In Proceedings of Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 2005: 3787-3791