基于机器学习的微钙化簇检测算法研究
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摘要
乳腺X线图像中的微钙化簇是早期乳腺癌的一个重要征象,这使得尽早检测微钙化簇并判断其是否有恶化倾向成为实现乳腺癌早期诊断的关键技术之一。然而,乳腺X线图像中只有3%的信息能为人眼所见,大量的信息是人眼不可辨识的,即便是经验丰富的临床医生也很难及时发现其中表征早期乳腺癌的微小钙化点,以致延误病人的最佳治疗时机。为了能够有效地检测出早期乳腺癌的隐匿性病征,更好地辅助医生发现早期乳腺癌,本文采用多分辨分析、子空间学习以及集成学习等机器学习领域最新的理论成果,针对女性的乳腺X线图像中微钙化簇增强和病灶检测问题进行了深入系统的研究,为早期乳腺癌的计算机辅助检测和诊断奠定了基础。论文取得了以下主要研究成果:
(1)乳腺区域结构复杂,要在乳腺X线图像中挑选能够完全囊括复杂区域特征的具有代表性的样本很难实现。为了解决该问题,本文提出了一种基于主动学习的微钙化簇区域检测新算法。首先利用方向差分滤波器组对微钙化区域进行增强和特征提取,同时抑制高亮血管和导管等复杂区域的干扰;然后利用基于Bootstrap主动学习方法进行样本的选择以提高分类器的性能;最后在乳腺X线图像中检测钙化簇区域。实验结果表明,该算法在保证较高检出率的同时有效地降低了假阳性率。
(2)为了提高微钙化簇检测器的泛化能力和运行效率,本文提出了一种基于子空间学习和双支持向量机(Twin Support Vector Machine, TWSVM)的微钙化簇检测新框架。该框架首先采用简单的伪影去除滤波器和高通滤波器来增强钙化点簇;然后将子空间学习算法嵌入到该框架中对待处理的图像块进行子空间特征提取;最后在特征子空间用TWSVM进行微钙化簇区域的检测。实验结果表明,该微钙化簇检测算法的泛化能力和处理速度得到了显著提升。
(3)为了充分利用图像中的空间结构信息,本文将基于向量的双支持向量机学习算法扩展到能够处理张量数据的双支持张量机(Twin Support Tensor Machine, TWSTM),并使之成功应用到微钙化簇的检测中。实验结果表明该算法检测性能优于TWSVM,并且能很好的处理小样本问题。
(4)为了将多个微钙化簇检测算法进行集成以获得比单个检测器更好的检测能力,本文设计了一种新的集成学习方法——Bracing,并将之应用到乳腺X线图像的微钙化簇检测。该算法将主动相关反馈嵌入到基学习器训练中以提升其泛化能力;并根据反馈的结果动态更新基学习器的权重。实验结果表明,Bracing算法提高了集成分类器的泛化能力,且在一定程度上避免了过拟合现象。
(5)由于子空间学习算法对训练数据中的噪声较为敏感,本文设计了一种基于混合多子空间选择性集成的方法,并将其应用到乳腺的钙化点簇检测。该方法根据子空间学习算法保留分类信息的能力,有选择地对其进行集成。实验结果表明该方法提高了微钙化簇检测算法的性能和稳定性,能更好地适应噪声环境。
综上,为了能够有效地检测出早期乳腺癌的隐匿性病征,更好地辅助医生发现早期乳腺癌,本文将机器学习方法在微钙化簇检测方面的应用进行了深入的研究和进一步的发展,所提出的方法能够有效地提高微钙化簇的检出率、降低假阳性率,为基于乳腺X线图像的辅助诊断系统的研究提供了新思路和新方法。
In digital mammograms, an important sign of the early breast cancer is the existence of microcalcification clusters (MCs). One of the key techniques for early diagnosis of the breast cancer is to detect MCs and to judge whether they are malignant or not in mammograms. However, there is only about 3% information in mammograms, which can be seen with the naked eye. Due to the most details in mammograms cannot been perceived by human eyes, it is even very difficult for an skillful radiologist to find the sign of early breast cancer, i.e., micalcification clusters, as a result missing the best time for treatment. To detect early sign of this disease and to aid doctors to diagnose breast cancer in early stage, we propose several new methods for enhancing and detecting the supicious areas by using some new techniques in machine learning, such as multi-resolution analysis, subspace learning, ensemble learning and so on. The main contributions of this paper are summarized as follows.
(1) Because of the complex structure in mammography images, it is very difficult to choose typical training samples, which include the complex structure features, from mammograms. To overcome this difficulty, a new approach to MCs detection is presented based on active machine learning. In the proposed algorithm, firstly the microcalcification region is enhanced with a directional difference filter bank, which effectively extracts the features of MCs and suppresses the blood vessels and mammary duts. Then the active sample selection method based on Bootstrap is employed to select training set. Finally the trained Bayesian classifier can be used to detect MCs in mammogram. The experimental results show that the proposed algorithm reduces false positive rate with keeping the same sensitivity.
(2) In order to improve the efficiency and the generalization ability of the MCs detector, a novel framework for MCs detection in mammograms is developed based on subspace learning and twin support vector machine (TWSVM). In the framework, MCs are firstly enhanced by using a simple-but-effective artifact removal filter and a well designed high-pass filter. Thereafter, subspace learning algorithms are embedded into this framework for subspace selection of each image block. Finally, the MCs detection procedure is performed in the feature subspaces. The experimental results show that MCs detection of the generalization ability and processing speed has been significantly increased.
(3) For the purpose of making full use of the image spatial structure information, we generalized the vector-based learning algorithm, twin support vector machine (TWSVM) into the tensor-based method, twin support tensor machines (TWSTM) and successfully apply it to MCs detection. The experimental results show that the proposed algorithm achieved better detection performance than the TWSVM-based one, and could also deal the small sample problem better.
(4) To get a better performance by ensembling the multiple methods for MCs detection rather than using a single algorithm, we developed a new ensemble learning method, Bracing, which has been applied to MCs detection in mammogram. In this method, when building new base learners the active relevance feedback is embedded to improve its generalization ability. Meanwhile, the weight of each base learner can be dynamically updated by the weighted score of the feedback result. Experimental results demonstrate that the Bracing algorithm could realize great advantages of ensemble classifier in generalization ability and could promise the preventing of overfitting.
(5) As the subspace learning algorithm is sensitive to noise in training dataset, we proposed a hybrid subspace selective ensemble (HSSE), and successfully applied it to MCs detection in mammograms. In the algorithm, the subspace learning algorithm will be selectively ensembled according to the ability of preserving the classification information. Experimental results show that the proposed method improved the performance and stability of MCs detection and could be adapt to the noise enviroments better.
In summary, to effectively detect the early sign of breast cancer in mammograms and to aid doctors to diagnose breast cancer better in early stage, we deeply studied the machine learning methods and their applications in microcalcification clusters detection. The proposed methods could get satisfactory results on sensitivity and reduce false positive rate, which provide some new ideas and methods for the research and development of computer-aided detection system in the breast cancer detection community.
(1)乳腺区域结构复杂,要在乳腺X线图像中挑选能够完全囊括复杂区域特征的具有代表性的样本很难实现。为了解决该问题,本文提出了一种基于主动学习的微钙化簇区域检测新算法。首先利用方向差分滤波器组对微钙化区域进行增强和特征提取,同时抑制高亮血管和导管等复杂区域的干扰;然后利用基于Bootstrap主动学习方法进行样本的选择以提高分类器的性能;最后在乳腺X线图像中检测钙化簇区域。实验结果表明,该算法在保证较高检出率的同时有效地降低了假阳性率。
(2)为了提高微钙化簇检测器的泛化能力和运行效率,本文提出了一种基于子空间学习和双支持向量机(Twin Support Vector Machine, TWSVM)的微钙化簇检测新框架。该框架首先采用简单的伪影去除滤波器和高通滤波器来增强钙化点簇;然后将子空间学习算法嵌入到该框架中对待处理的图像块进行子空间特征提取;最后在特征子空间用TWSVM进行微钙化簇区域的检测。实验结果表明,该微钙化簇检测算法的泛化能力和处理速度得到了显著提升。
(3)为了充分利用图像中的空间结构信息,本文将基于向量的双支持向量机学习算法扩展到能够处理张量数据的双支持张量机(Twin Support Tensor Machine, TWSTM),并使之成功应用到微钙化簇的检测中。实验结果表明该算法检测性能优于TWSVM,并且能很好的处理小样本问题。
(4)为了将多个微钙化簇检测算法进行集成以获得比单个检测器更好的检测能力,本文设计了一种新的集成学习方法——Bracing,并将之应用到乳腺X线图像的微钙化簇检测。该算法将主动相关反馈嵌入到基学习器训练中以提升其泛化能力;并根据反馈的结果动态更新基学习器的权重。实验结果表明,Bracing算法提高了集成分类器的泛化能力,且在一定程度上避免了过拟合现象。
(5)由于子空间学习算法对训练数据中的噪声较为敏感,本文设计了一种基于混合多子空间选择性集成的方法,并将其应用到乳腺的钙化点簇检测。该方法根据子空间学习算法保留分类信息的能力,有选择地对其进行集成。实验结果表明该方法提高了微钙化簇检测算法的性能和稳定性,能更好地适应噪声环境。
综上,为了能够有效地检测出早期乳腺癌的隐匿性病征,更好地辅助医生发现早期乳腺癌,本文将机器学习方法在微钙化簇检测方面的应用进行了深入的研究和进一步的发展,所提出的方法能够有效地提高微钙化簇的检出率、降低假阳性率,为基于乳腺X线图像的辅助诊断系统的研究提供了新思路和新方法。
In digital mammograms, an important sign of the early breast cancer is the existence of microcalcification clusters (MCs). One of the key techniques for early diagnosis of the breast cancer is to detect MCs and to judge whether they are malignant or not in mammograms. However, there is only about 3% information in mammograms, which can be seen with the naked eye. Due to the most details in mammograms cannot been perceived by human eyes, it is even very difficult for an skillful radiologist to find the sign of early breast cancer, i.e., micalcification clusters, as a result missing the best time for treatment. To detect early sign of this disease and to aid doctors to diagnose breast cancer in early stage, we propose several new methods for enhancing and detecting the supicious areas by using some new techniques in machine learning, such as multi-resolution analysis, subspace learning, ensemble learning and so on. The main contributions of this paper are summarized as follows.
(1) Because of the complex structure in mammography images, it is very difficult to choose typical training samples, which include the complex structure features, from mammograms. To overcome this difficulty, a new approach to MCs detection is presented based on active machine learning. In the proposed algorithm, firstly the microcalcification region is enhanced with a directional difference filter bank, which effectively extracts the features of MCs and suppresses the blood vessels and mammary duts. Then the active sample selection method based on Bootstrap is employed to select training set. Finally the trained Bayesian classifier can be used to detect MCs in mammogram. The experimental results show that the proposed algorithm reduces false positive rate with keeping the same sensitivity.
(2) In order to improve the efficiency and the generalization ability of the MCs detector, a novel framework for MCs detection in mammograms is developed based on subspace learning and twin support vector machine (TWSVM). In the framework, MCs are firstly enhanced by using a simple-but-effective artifact removal filter and a well designed high-pass filter. Thereafter, subspace learning algorithms are embedded into this framework for subspace selection of each image block. Finally, the MCs detection procedure is performed in the feature subspaces. The experimental results show that MCs detection of the generalization ability and processing speed has been significantly increased.
(3) For the purpose of making full use of the image spatial structure information, we generalized the vector-based learning algorithm, twin support vector machine (TWSVM) into the tensor-based method, twin support tensor machines (TWSTM) and successfully apply it to MCs detection. The experimental results show that the proposed algorithm achieved better detection performance than the TWSVM-based one, and could also deal the small sample problem better.
(4) To get a better performance by ensembling the multiple methods for MCs detection rather than using a single algorithm, we developed a new ensemble learning method, Bracing, which has been applied to MCs detection in mammogram. In this method, when building new base learners the active relevance feedback is embedded to improve its generalization ability. Meanwhile, the weight of each base learner can be dynamically updated by the weighted score of the feedback result. Experimental results demonstrate that the Bracing algorithm could realize great advantages of ensemble classifier in generalization ability and could promise the preventing of overfitting.
(5) As the subspace learning algorithm is sensitive to noise in training dataset, we proposed a hybrid subspace selective ensemble (HSSE), and successfully applied it to MCs detection in mammograms. In the algorithm, the subspace learning algorithm will be selectively ensembled according to the ability of preserving the classification information. Experimental results show that the proposed method improved the performance and stability of MCs detection and could be adapt to the noise enviroments better.
In summary, to effectively detect the early sign of breast cancer in mammograms and to aid doctors to diagnose breast cancer better in early stage, we deeply studied the machine learning methods and their applications in microcalcification clusters detection. The proposed methods could get satisfactory results on sensitivity and reduce false positive rate, which provide some new ideas and methods for the research and development of computer-aided detection system in the breast cancer detection community.
引文
[1] American Cancer Society. Breast Cancer Facts & Figures 2007-2008 [M]. Atlanta; American Cancer Society. 2007.
[2] Bagci A M, Cetin A E. Detection of microcalcifications in mammograms using local maxima and adaptive wavelet transform analysis [J]. Electronics Letters, 2002, 38(22): 1311-1313.
[3] Bagci A M, Yardimci Y, Cetin A E. Detection of microcalcification clusters in mammogram images using local maxima and adaptive wavelet transform analysis[C]. in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP 2002), 2002: 3856-3859.
[4] Ben Hamad N, Taouil K. Exploring wavelets subband decomposition toward a computer aided detection of micro calcification in breast cancer[C]. in Proceedings of the 2nd International Conference on Distributed Frameworks for Multimedia Applications, 2007: 229-236.
[5] Bocchi L, Nori J. Shape analysis of microcalcifications using Radon transform [J]. Medical Engineering & Physics, 2007, 29(6): 691-698.
[6] Boccignone G, Chianese A, Picariello A. Computer aided detection of microcalcifications in digital mammograms [J]. Computers in Biology andMedicine, 2000, 30(5): 267-286.
[7] Cahoon T C, Sutton M A, Bezdek J C. Breast cancer detection using image processing techniques[C]. in Proceedings of the IEEE International Conference on Fuzzy Systems, 2000: 973-976.
[8] Chan H-P, Sahiner B, Lam K L, et al. Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces [J]. Medical Physics, 1998, 25(10): 2007-2019.
[9] Chan H P, Doi K, Vybrony C J, et al. Improvement in Radiologists' Detection of Clustered Microcalcifications on Mammograms: The Potential of Computer-Aided Diagnosis [J]. Investigative Radiology, 1990, 25(10): 1102-1110.
[10] Chao W, Wei J, Xifeng D. Characterization of clustered microcalcifications in mammograms based on support vector machines with genetic algorithms[C]. in Proceedings of the International Symposium on Biophotonics, Nanophotonics and Metamaterials, Metamaterials, 2006: 114-117.
[11] Cheng H-D, Lui Y M, Freimanis R I. A novel approach to microcalcification detection using fuzzy logic technique [J]. IEEE Trans Med Imag, 1998, 17(3): 442-450.
[12] Cheng H D, Cai X, Chen X, et al. Computer-aided detection and classification of microcalcifications in mammograms: a survey [J]. Pattern Recognition, 2003, 36(12): 2967-2991.
[13] Cheng H D, Chen J R, Freimanis R I, et al. A novel fuzzy logic approach to microcalcification detection [J]. Information Sciences, 1998, 111(1-4): 189-205.
[14] Cheng H D, Wang J, Shi X. Microcalcification detection using fuzzy logic and scale space approaches [J]. Pattern Recognition, 2004, 37(2): 363-375.
[15] Cortes C, Vapnik V. Support-vector networks [J]. Machine learning, 1995, 20(3): 273-297.
[16] Davies D H, Dance D R. Automatic computer detection of clustered calcifications in digital mammograms [J]. Physics in Medicine & Biology, 1990, 35(8): 1111-1118.
[17] De Santo M, Molinara M, Tortorella F, et al. Automatic classification of clustered microcalcifications by a multiple expert system [J]. Pattern Recognition, 2003, 36(7): 1467-1477.
[18] Dengler J, Behrens S, Desaga J F. Segmentation of microcalcifications in mammograms [J]. IEEE Trans Med Imag, 1993, 12(4): 634-642.
[19] Dhawan A P, Chitre Y, Kaiser-Bonasso C. Analysis of mammographic microcalcifications using gray-level image structure features [J]. IEEE Trans Med Imag, 1996, 15(3): 246-259.
[20] Doi K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential [J]. Computerized Medical Imaging and Graphics, 2007, 31(4-5): 198-211.
[21] El-Naqa I, Yongyi Y, Wernick M N, et al. A support vector machine approach fordetection of microcalcifications [J]. IEEE Trans Med Imag, 2002, 21(12): 1552-1563.
[22] Elter M, Horsch A. CADx of mammographic masses and clustered microcalcifications: A review [J]. Medical Physics, 2009, 36(6): 2052-2068.
[23] Ema T, Doi K, Nishikawa R M, et al. Image feature analysis and computer-aided diagnosis in mammography: Reduction of false-positive clustered microcalcifications using local edge-gradient analysis [J]. Medical Physics, 1995, 22(2): 161-169.
[24] Fam B W, Olson S L, Winter P F, et al. Algorithm for the detection of fine clustered calcifications on film mammograms [J]. Radiology, 1988, 169(2): 333-337.
[25] Fu J C, Lee S K, Wong S T C, et al. Image segmentation feature selection and pattern classification for mammographic microcalcifications [J]. Computerized Medical Imaging and Graphics, 2005, 29(6): 419-429.
[26] Garcia-Orellana C J, Gallardo-Caballero R, Macias-Macias M, et al. SVM and Neural Networks comparison in mammographic CAD[C]. in Proceedings of the 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, 2007: 3204-3207.
[27] Gavrielides M A, Lo J Y, Floyd J C E. Parameter optimization of a computer-aided diagnosis scheme for the segmentation of microcalcification clusters in mammograms [J]. Medical Physics, 2002, 29(4): 475-483.
[28] Gavrielides M A, Lo J Y, Vargas-Voracek R, et al. Segmentation of suspicious clustered microcalcifications in mammograms [J]. Medical Physics, 2000, 27(1): 13-22.
[29] Gunawan D. Microcalcification detection using wavelet transform[C]. in Proceedings of the IEEE Pacific RIM Conference on Communications, Computers, and Signal Processing, 2001: 694-697.
[30] Gurcan M N, Chan H-P, Sahiner B, et al. Optimal Neural Network Architecture Selection: Improvement in Computerized Detection of Microcalcifications [J]. Academic Radiology, 2002, 9(4): 420-429.
[31] Halkiotis S, Botsis T, Rangoussi M. Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks [J]. Signal Processing, 2007, 87(7): 1559-1568.
[32] Heath M, Bowyer K, Kopans D, et al. The digital database for screening mammography [J]. Int Work on Dig Mammography, 2000, 212–218.
[33] Highnam R, Brady M. Mammographic image analysis [M]. Dordrecht: Kluwer Academic Pub, 1999.
[34] Hojjatoleslami A, Sardo L, Kittler J. RBF based classifier for the detection of microcalcifications in mammograms with outlier rejection capability[C]. in Proceedings of the IEEE International Conference on Neural Networks, 1997: 1379-1384.
[35] Huang Y-K, Yu S-N. Recognition of microcalcifications in digital mammogramsbased on Markov random field and deterministic fractal modeling[C]. in Proceedings of the 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, 2007: 3922-3925.
[36] iCAD website. http://www.icadmed.com/ [M]. 2008.
[37] Jamarani S M H, Rezai-rad G, Behnam H. A novel method for breast cancer prognosis using wavelet packet based neural network[C]. in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology, 2005: 3414-3417.
[38] Jayadeva R, Chandra S. Twin Support Vector Machines for Pattern Classification [J]. IEEE Trans Pattern Anal Mach Intell, 2007, 905-910.
[39] Jesneck J L, Nolte L W, Baker J A, et al. Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis [J]. Medical Physics, 2006, 33(8): 2945-2954.
[40] Jiang J, Yao B, Wason A M. Integration of fuzzy logic and structure tensor towards mammogram contrast enhancement [J]. Computerized Medical Imaging and Graphics, 2005, 29(1): 83-90.
[41] Jiang Y, Nishikawa R M, Papaioannou J. Dependence of computer classification of clustered microcalcifications on the correct detection of microcalcifications [J]. Medical Physics, 2001, 28(9): 1949-1957.
[42] Juarez L C, Ponomaryov V, Sanchez J L R. Detection of microcalcifications in digital mammograms images using wavelet transform[C]. in Proceedings of the Electronics, Robotics and Automotive Mechanics Conference, 2006: 58-61.
[43] Kahn E, Gavoille A, Masselot J. Computer analysis of breast calcifications in mammographic images[C]. in Proceedings of the Proceedings of the International Symposium on Computer Assisted Radiology, 1987: 729-733.
[44] Kallergi M. Computer-aided diagnosis of mammographic microcalcification clusters [J]. Medical Physics, 2004, 31(2): 314-326.
[45] Karssemeijer N. Stochastic model for automated detection of calcifications in digital mammograms [J]. Image and Vision Computing, 1992, 10(6): 369-375.
[46] Karssemeijer N. Adaptive Noise Equalization and Recognition of Microcalcification Clusters in Mammograms [J]. International Journal of Pattern Recognition and Artificial Intelligence, 1993, 7(6): 1357-1376.
[47] Kim J K, Park H W. Surrounding region dependence method for detection of clustered microcalcifications on mammograms[C]. in Proceedings of the IEEE International Conference on Image Processing, 1997: 535-538.
[48] Kocur C M, Rogers S K, Myers L R, et al. Using neural networks to select wavelet features for breast cancer diagnosis [J]. IEEE Eng Med Biol Mag, 1996, 15(3): 95-102, 108.
[49] Kopans D B. Breast imaging [M]. 3rd ed. New York: Lippincott Williams & Wilkins, 2006.
[50] Lassetn C, Bonadona V. Medical management of women with inherited predisposition to breast cancer: indications and procedures for mammographicscreening [J]. Bull Cancer, 2001, 88(7): 677.
[51] Le Gal M, Chavanne G, Pellier D. Diagnostic value of clustered microcalcifications discovered by mammography (apropos of 227 cases with histological verification and without a palpable breast tumor) [J]. Bulletin du cancer, 1984, 71(1): 57-57.
[52] Lefebvre F, Benali H, Gilles R, et al. A fractal approach to the segmentation of microcalcifications in digital mammograms [J]. Medical Physics, 1995, 22(4): 381-390.
[53] Lei Z, Xieping G. Research on translation-invariant wavelet transform for classification in mammograms[C]. in Proceedings of the Third International Conference on Natural Computation, 2007: 571-575.
[54] Li M, Zhou Z-H. Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples [J]. IEEE Trans Syst, Man, Cybern A, Syst, Humans, 2007, 37(6): 1088-1098.
[55] Lim K J, Choi C S, Yoon D Y, et al. Computer-Aided Diagnosis for the Differentiation of Malignant from Benign Thyroid Nodules on Ultrasonography [J]. Academic Radiology, 2008, 15(7): 853-858.
[56] Lo S C B, Li H, Freedman M T. Optimization of wavelet decomposition for image compression and feature preservation [J]. IEEE Trans Med Imag, 2003, 22(9): 1141-1151.
[57] Lopez-Aligue F J, Acevedo-Sotoca I, Garcia-Manso A, et al. Microcalcifications detection in digital mammograms[C]. in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology, 2004: 1577-1580.
[58] Mascio L N. Automated analysis for microcalcifications in high resolution digital mammograms[M]. US Patent 5586160, 1996.
[59] Mencattini A, Caselli F, Salmeri M, et al. Wavelet based adaptive algorithm for mammographic images enhancement and denoising[C]. in Proceedings of the International Conference on Image Processing(ICIP 2005), 2005: 1141-1144.
[60] Mencattini A, Salmeri M, Lojacono R, et al. Mammographic images enhancement and denoising for microcalcification detection using dyadic wavelet processing[C]. in Proceedings of the IEEE Instrumentation and Measurement Technology Conference, 2006: 49-53.
[61] Mencattini A, Salmeri M, Lojacono R, et al. Mammographic Images Enhancement and Denoising for Breast Cancer Detection Using Dyadic Wavelet Processing [J]. IEEE Trans Instrumentation and Measurement, 2008, 57(7): 1422-1430.
[62] Mousa R, Munib Q, Moussa A. Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural [J]. Expert Systems with Applications, 2005, 28(4): 713-723.
[63] Nakayama R, Uchiyama Y, Yamamoto K, et al. Computer-aided diagnosis scheme using a filter bank for detection of microcalcification clusters in mammograms [J]. IEEE Trans Biomed Eng, 2006, 53(2): 273-283.
[64] National Cancer Institute. Breast Cancer Screening (http://www.cancer.gov/ cancertopics/pdq/screening/breast/Patient/page3) [M]. 2008.
[65] Nishikawa R M. Current status and future directions of computer-aided diagnosis in mammography [J]. Computerized Medical Imaging and Graphics, 2007, 31(4-5): 224-235.
[66] Pal N R, Bhowmick B, Patel S K, et al. A multi-stage neural network aided system for detection of microcalcifications in digitized mammograms [J]. Neurocomputing, 2008, 71(13-15): 2625-2634.
[67] Papadopoulos A, Fotiadis D I, Costaridou L. Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques [J]. Computers in Biology and Medicine, 2008, 38(10): 1045-1055.
[68] Papadopoulos A, Fotiadis D I, Likas A. An automatic microcalcification detection system based on a hybrid neural network classifier [J]. Artificial Intelligence in Medicine, 2002, 25(2): 149-167.
[69] Papadopoulos A, Fotiadis D I, Likas A. Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines [J]. Artificial Intelligence in Medicine, 2005, 34(2): 141-150.
[70] Peng Y, Huang Q, Jiang P, et al. Cost-sensitive ensemble of support vector machines for effective detection of microcalcification in breast cancer diagnosis[C]. 2005: 483-493.
[71] Qian W, Kallergi M, Clarke L P, et al. Tree structured wavelet transform segmentation of microcalcifications in digital mammography [J]. Medical Physics, 1995, 22(8): 1247-1254.
[72] R2 Technology website. http://www.r2tech.com/ [M]. 2009.
[73] Rangayyan R M, Ayres F J, Leo Desautels J E. A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs [J]. Journal of the Franklin Institute, 2007, 344(3-4): 312-348.
[74] Regentova E, Lei Z, Jun Z, et al. Detecting microcalcifications in digital mammograms using wavelet domain hidden Markov Tree Model[C]. in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology, 2006: 1972-1975.
[75] Regentova E, Zhang L, Zheng J, et al. Microcalcification detection based on wavelet domain hidden Markov tree model: Study for inclusion to computer aided diagnostic prompting system [J]. Medical Physics, 2007, 34(6): 2206-2219.
[76] Retico A, Delogu P, Fantacci M E, et al. A scalable computer-aided detection system for microcalcification cluster identification in a pan-European distributed database of mammograms [J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2006, 569(2): 601-605.
[77] Rezai-Rad G, Jamarani S. Detecting microcalcification clusters in digital mammograms using combination of wavelet and neural network[C]. in Proceedings of the Conference on Computer Graphics, Imaging and Vision: NewTrends 2005, 2005: 197-201.
[78] Riyahi-Alam N, Ahmadian A, Tehrani J N, et al. Segmentation of suspicious clustered microcalcifications on digital mammograms: Using fuzzy logic and wavelet coefficients[C]. in Proceedings of the 7th International Conference on Signal Processing, 2004: 2228-2230.
[79] Riyahi-Alam N, Ahmadian A, Tehrani J N, et al. Segmentation of suspicious clustered microcalcifications on digital mammograms: Using Fuzzy logic and wavelet coefficients[C]. 2004: 2226-2228.
[80] Sajda P, Spence C, Pearson J. Learning contextual relationships in mammograms using a hierarchical pyramid neural network [J]. IEEE Trans Med Imag, 2002, 21(3): 239-250.
[81] Salvado J, Roque B. Detection of calcifications in digital mammograms using wavelet analysis and contrast enhancement[C]. in Proceedings of the IEEE International Workshop on Intelligent Signal Processing, 2005: 200-205.
[82] Sampat M P, Markey M K, Bovik A C. Computer-aided detection and diagnosis in mammography [J]. Handbook of Image and Video Processing, 2005, 10(4): 1195-1217.
[83] Schapire R, Freund Y. A decision theoretic generalization of on-line learning and an application to boosting [J]. Journal of Computer System Sciences, 1997, 55(1): 119-139.
[84] Sehad S, Desarnaud S, Strauss A. Artificial neural classification of clustered microcalcifications on digitized mammograms[C]. in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 1997: 4217-4222.
[85] Shen L, Rangayyan R M, Desautels J E L. Application of shape analysis to mammographic calcifications [J]. IEEE Trans Med Imag, 1994, 13(2): 263-274.
[86] Song L, Wang Q, Gao J. Microcalcification detection using combination of wavelet transform and morphology[C]. in Proceedings of the International Conference on Signal Processing Proceedings, 2007: 16-20.
[87] Sorantin E, Schmidt F, Mayer H, et al. Computer aided diagnosis of clustered microcalcifications using artificial neural nets [J]. Journal of Computing and Information Technology, 2000, 8(2): 151-160.
[88] Spiesberger W, Forschungslaboratorium P G H. Mammogram inspection by computer [J]. IEEE Transactions on Biomedical Engineering, 1979, BME-26(4): 213-219.
[89] Strickland R N, Hahn H I. Wavelet transforms for detecting microcalcifications in mammograms [J]. IEEE Trans Med Imag, 1996, 15(2): 218-229.
[90] Strickland R N, Lukins G J. Fuzzy system improves the performance of wavelet-based correlation detectors[C]. in Proceedings of the IEEE International Conference on Image Processing, 1997: 404-407.
[91] Taylor P, Champness J, Given-Wilson R, et al. Impact of computer-aided detection prompts on the sensitivity and specificity of screening mammography [J]. Health Technology Assessment, 2005, 9(6): 1-70.
[92] Thangavel K, Karnan M, Sivakumar R, et al. Automatic Detection of Microcalcifications in Mammograms-A Review [J]. ICGST International Journal on Graphics, Vision and Image Processing, 2005, 5(5): 31-61.
[93] Tipping M E. Sparse Bayesian Learning and the Relevance Vector Machine [J]. Journal of Machine Learning Research, 2001, 1(3): 211-244.
[94] Tsujii O, Freedman M T, Mun S K. Classification of microcalcifications in digital mammograms using trend-oriented radial basis function neural network [J]. Pattern Recognition, 1999, 32(5): 891-903.
[95] Veldkamp W J H, Karssemeijer N, Otten J D M, et al. Automated classification of clustered microcalcifications into malignant and benign types [J]. Medical Physics, 2000, 27(11): 2600-2608.
[96] Verma B. Neural network based technique to locate and classify microcalcifications in digital mammograms[C]. in Proceedings of the IEEE International Conference on Neural Networks, 1998: 1790-1793.
[97] Verma B, Zakos J. A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques [J]. IEEE Trans Inf Technol Biomed, 2001, 5(1): 46-54.
[98] Vibert J F, Valleron A J. Automatic detection of microcalcifications in mammography using a neuromimetic system based on retina [J]. Studies in health technology and informatics, 2003, 95(1): 589-594.
[99] Wang J, Cheng H D. Fuzzy logic and scale space approach to microcalcification detection[C]. in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP 2003), 2003: 345-348.
[100] Wang T, Karayiannis N. Detection of microcalcifications in digital mammograms using wavelets [J]. IEEE Trans Med Imag, 1998, 17(4): 498-509.
[101] Warren R. Screening women at high risk of breast cancer on the basis of evidence [J]. European Journal of Radiology, 2001, 39(1): 50-59.
[102] Wei L, Yang Y, Nishikawa R M. Retrieval-driven microcalcification classification for breast cancer diagnosis[C]. in Proceedings of the 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007: 1260-1263.
[103] Wei L, Yang Y, Nishikawa R M. Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis [J]. Pattern Recognition, 2009, 42(6): 1126-1132.
[104] Wei L, Yang Y, Nishikawa R M, et al. A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications [J]. IEEE Trans Med Imag, 2005, 24(3): 371-380.
[105] Wei L, Yang Y, Nishikawa R M, et al. Relevance vector machine for automatic detection of clustered microcalcifications [J]. IEEE Trans Med Imag, 2005, 24(10): 1278-1285.
[106] World Health Organization. Cancer (http://www.who.int/mediacentre/factsheets/ fs297/en/index.html) [M]. 2009.
[107] Yang T, Guo S, Wu X, et al. An approach based on immune algorithm and SVM for detection and classification of microcalcifications[C]. in Proceedings of the 1st International Conference on Bioinformatics and Biomedical Engineering, 2007: 588-591.
[108] Yoshida H, Doi K, Nishikawa R M, et al. An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms [J]. Academic Radiology, 1996, 3(8): 621-627.
[109] Yu S, Guan L. A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films [J]. IEEE Trans Med Imag, 2000, 19(2): 115-126.
[110] Zhang L, Qian W, Sankar R, et al. A new false positive reduction method for MCCs detection in digital mammography[C]. in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP 2001), 2001: 1033-1036.
[111] Zhang L, Sankar R, Qian W. Advances in micro-calcification clusters detection in mammography [J]. Computers in Biology and Medicine, 2002, 32(6): 515-528.
[112] Zhang W, Doi K, Giger M L, et al. Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network [J]. Medical Physics, 1994, 21(4): 517-524.
[113] Zhang X-P, Desai M D, Shah R B. Automatic wavelet-based detection of clustered microcalcifications[C]. in Proceedings of the Southern Biomedical Engineering Conference, 1998: 75-75.
[114] Zheng B, Qian W, Clarke L P. Digital mammography: mixed feature neural network with spectral entropy decision for detection of microcalcifications [J]. IEEE Trans Med Imag, 1996, 15(5): 589-597.
[115] Zolghadrasli A R, Maghsoodzadeh Z. Modification of mammograms for early diagnosis of breast cancer using wavelet and neural networks[C]. in Proceedings of the IEEE International Conference on Computer Systems and Applications, 2006: 288-295.
[116]万柏坤,王瑞平,朱欣.基于综合处理方法的乳腺X影像中微钙化点检测新技术[J].中国生物医学工程学报, 2002, 21(06): 57-63.
[117]万柏坤,王瑞平,朱欣, et al. SVM算法及其在乳腺X片微钙化点自动检测中的应用[J].电子学报, 2004, 32(04): 61-64.
[118]李树楠,万柏坤,马振鹤, et al.基于小波变换的乳腺X线影像微钙化点感兴趣区域提取新技术[J].生物医学工程学杂志, 2005, 22(02): 144-146.
[119]胡正平,张晔.基于可拒识-双层支持向量分类器的微钙化点检测[J].中国图象图形学报, 2006, 05): 58-61.
[120]胡正平,张晔.基于快速可拒识-双层支持向量分类器的微钙化点的检测算法[J].仪器仪表学报, 2007, 03): 64-68.
[121]胡正平,张晔.基于微弱目标-背景合成技术的微钙化点检测[J].中国生物医学工程学报, 2007, 04): 160-163.
[122]陈磊,张凯,刘克钦.基于模糊C均值和小波多分辨分析的数字乳房X射线片钙化点提取算法研究[J].生物医学工程学进展, 2009, 30(01): 20-22.
[123]马慧彬,张海燕,丁晓迪. Top-hat与SVM在乳腺微钙化点检测中的应用[J].计算机工程与应用, 2009, 45(20): 247-249.
[1] Bankman I N, Nizialek T, Simon I, et al. Segmentation algorithms for detecting microcalcifications in mammograms [J]. IEEE Trans Inf Technol Biomed, 1997, 1(2): 141-149.
[2] Baram Y, El-Yaniv R, Luz K. Online choice of active learning algorithms [J]. The Journal of Machine Learning Research, 2004, 5(1): 255-291.
[3] Boccignone G, Chianese A, Picariello A. Computer aided detection of microcalcifications in digital mammograms [J]. Computers in Biology and Medicine, 2000, 30(5): 267-286.
[4] Cohn D, Atlas L, Ladner R. Improving generalization with active learning [J]. Machine learning, 1994, 15(2): 201-221.
[5] Cohn D, Ghahramani Z, Jordan M. Active learning with statistical models [J]. Journal of artificial intelligence research, 1996, 129-145.
[6] Cooklev T, Nishihara A, Sablatash M. Regular orthonormal and biorthogonal wavelet filters [J]. Signal Processing, 1997, 57(2): 121-137.
[7] Daubechies I. Ten lectures on wavelets [M]. Philadelphia, PA: Society for Industrial Mathematics, 1992.
[8] Domingos P, Pazzani M. Beyond independence: Conditions for the optimality of the simple Bayesian classifier [J]. Machine learning, 1996, 24(3): 105-112.
[9] Domingos P, Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss [J]. Machine learning, 1997, 29(2): 103-130.
[10] Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers [J]. Machine learning, 1997, 29(2): 131-163.
[11] Halkiotis S, Botsis T, Rangoussi M. Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks [J]. Signal Processing, 2007, 87(7): 1559-1568.
[12] Jin H, Kobatake H. Extraction of microcalcifications from mammograms using morphological filter with multiple structuring elements [J]. Systems and Computers in Japan, 1993, 24(11): 66-74.
[13] Mallat S. A theory for multiresolution signal decomposition: The wavelet representation [J]. IEEE Trans Pattern Anal Mach Intell, 1989, 11(7): 674-693.
[14] McLoughlin K J, Bones P J, Karssemeijer N. Noise equalization for detection of microcalcification clusters in direct digital mammogram images [J]. IEEE Trans Med Imag, 2004, 23(3): 313-320.
[15] Nakayama R, Uchiyama Y, Yamamoto K, et al. Computer-aided diagnosis scheme using a filter bank for detection of microcalcification clusters in mammograms [J]. IEEE Trans Biomed Eng, 2006, 53(2): 273-283.
[16] Qian W, Kallergi M, Clarke L P, et al. Tree structured wavelet transform segmentation of microcalcifications in digital mammography [J]. Medical Physics, 1995, 22(8): 1247-1254.
[17] Shimizu A, Toriwaki J, Hasegawa J. Characteristics of rotatory second order difference filter for computer aided diagnosis of medical images [J]. Systems and Computers in Japan, 1995, 26(11): 38-51.
[18] Strickland R, Hahn H. Wavelet transforms for detecting microcalcifications in mammograms [J]. IEEE Trans Med Imag, 1996, 15(2): 218-229.
[19] Strickland R, Hahn H. Wavelet transform methods for object detection and recovery [J]. IEEE Trans Image Process, 1997, 6(5): 724-735.
[20] Strickland R N, Hahn H I. Wavelet transforms for detecting microcalcifications in mammograms [J]. IEEE Trans Med Imag, 1996, 15(2): 218-229.
[21] Tilbury J, Van Eetvelt W, Garibaldi J, et al. Receiver operating characteristic analysis for intelligent medical systems-a new approach for finding confidence intervals [J]. IEEE Trans Biomed Eng, 2000, 47(7): 952-963.
[22] Valiant L G. A theory of the learnable [J]. Communications of the ACM, 1984, 27(11): 1134-1142.
[23] Wang T, Karayiannis N. Detection of microcalcifications in digital mammograms using wavelets [J]. IEEE Trans Med Imag, 1998, 17(4): 498-509.
[24] Winston P, Learning Structural Descriptions from Examples (AITR-231) [R]. Cambridge, MA, USA: Massachusetts Institute of Technology, 1970.
[25] Yoshida H, Doi K, Nishikawa R, et al. An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms [J]. Academic Radiology, 1996, 3(8): 621.
[26] Yu S, Guan L. A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films [J]. IEEE Trans Med Imag, 2000, 19(2): 115-126.
[1] http://marathon.csee.usf.edu/Mammography/Database.html [M].
[2] Bagci A M, Cetin A E. Detection of microcalcifications in mammograms using local maxima and adaptive wavelet transform analysis [J]. Electronics Letters, 2002, 38(22): 1311-1313.
[3] Bagnall M J C, Evans A J, Wilson A R M, et al. Predicting Invasion in Mammographically Detected Microcalcification [J]. Clinical Radiology, 2001, 56(10): 828-832.
[4] Bocchi L, Coppini G, Nori J, et al. Detection of single and clustered microcalcifications in mammograms using fractals models and neural networks [J]. Medical Engineering & Physics, 2004, 26(4): 303-312.
[5] Cai D, He X, Han J, Subspace learning based on tensor analysis(UIUCDCS-R- 2005-2572) [R]: Dept. of Computer Science Tech., Univ. of Illinois at Urbana- Champaign, 2005.
[6] Chen C H, Lee G G. On Digital mammogram segmentation and microcalcification detection using multiresolution wavelet analysis [J]. Graphical Models and Image Processing, 1997, 59(5): 349-364.
[7] Cheng H-D, Lui Y M, Freimanis R I. A novel approach to microcalcification detection using fuzzy logic technique [J]. IEEE Trans Med Imag, 1998, 17(3): 442-450.
[8] Cheng H D, Cai X, Chen X, et al. Computer-aided detection and classification of microcalcifications in mammograms: a survey [J]. Pattern Recognition, 2003, 36(12): 2967-2991.
[9] Cheng H D, Chen J R, Freimanis R I, et al. A novel fuzzy logic approach to microcalcification detection [J]. Information Sciences, 1998, 111(1-4): 189-205.
[10] Cheng H D, Wang J, Shi X. Microcalcification detection using fuzzy logic and scale space approaches [J]. Pattern Recognition, 2004, 37(2): 363-375.
[11] El-Naqa I, Yongyi Y, Wernick M N, et al. A support vector machine approach for detection of microcalcifications [J]. IEEE Trans Med Imag, 2002, 21(12): 1552-1563.
[12] Fukunaga K. Introduction to Statistical Pattern Recognition [M]. 2nd ed. Boston: Academic Press, 1990.
[13] Gulsrud T O, Husoy J H. Optimal filter-based detection of microcalcifications [J]. IEEE Trans Biomed Eng, 2001, 48(11): 1272-1281.
[14] Gurcan M N, Chan H-P, Sahiner B, et al. Optimal Neural Network Architecture Selection: Improvement in Computerized Detection of Microcalcifications [J]. Academic Radiology, 2002, 9(4): 420-429.
[15] Halkiotis S, Botsis T, Rangoussi M. Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks [J]. Signal Processing, 2007, 87(7): 1559-1568.
[16] He X, Cai D, Niyogi P. Tensor Subspace Analysis[C]. in Proceedings of the Advances in Neural Information Processing Systems( NIPS 2005), 2005: 499-506.
[17] Jayadeva R, Chandra S. Twin support vector machines for pattern classification [J]. IEEE Trans Pattern Anal Mach Intell, 2007, 905-910.
[18] Jiang J, Yao B, Wason A M. Integration of fuzzy logic and structure tensor towards mammogram contrast enhancement [J]. Computerized Medical Imaging and Graphics, 2005, 29(1): 83-90.
[19] Kocur C M, Rogers S K, Myers L R, et al. Using neural networks to select wavelet features for breast cancer diagnosis [J]. IEEE Eng Med Biol Mag, 1996, 15(3): 95-102, 108.
[20] Kopans D B. Breast imaging [M]. 3rd ed. New York: Lippincott Williams & Wilkins, 2006.
[21] Mencattini A, Salmeri M, Lojacono R, et al. Mammographic Images Enhancement and Denoising for Breast Cancer Detection Using Dyadic Wavelet Processing [J]. IEEE Trans Instrumentation and Measurement, 2008, 57(7): 1422-1430.
[22] Mousa R, Munib Q, Moussa A. Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural [J]. Expert Systems with Applications, 2005, 28(4): 713-723.
[23] Nakayama R, Uchiyama Y, Yamamoto K, et al. Computer-aided diagnosis scheme using a filter bank for detection of microcalcification clusters in mammograms [J]. IEEE Trans Biomed Eng, 2006, 53(2): 273-283.
[24] Pal N R, Bhowmick B, Patel S K, et al. A multi-stage neural network aided system for detection of microcalcifications in digitized mammograms [J].Neurocomputing, 2008, 71(13-15): 2625-2634.
[25] Papadopoulos A, Fotiadis D I, Likas A. An automatic microcalcification detection system based on a hybrid neural network classifier [J]. Artificial Intelligence in Medicine, 2002, 25(2): 149-167.
[26] Papadopoulos A, Fotiadis D I, Likas A. Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines [J]. Artificial Intelligence in Medicine, 2005, 34(2): 141-150.
[27] Regentova E, Zhang L, Zheng J, et al. Microcalcification detection based on wavelet domain hidden Markov tree model: Study for inclusion to computer aided diagnostic prompting system [J]. Medical Physics, 2007, 34(6): 2206-2219.
[28] Rose C, Turi D, Williams A, et al. Web Services for the DDSM and Digital Mammography Research [M]. Digital Mammography. 2006: 376-383.
[29] Sajda P, Spence C, Pearson J. Learning contextual relationships in mammograms using a hierarchical pyramid neural network [J]. IEEE Trans Med Imag, 2002, 21(3): 239-250.
[30] Soltanian-Zadeh H, Rafiee-Rad F, Pourabdollah-Nejad D S. Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms [J]. Pattern Recognition, 2004, 37(10): 1973-1986.
[31] Strickland R N, Hahn H I. Wavelet transforms for detecting microcalcifications in mammograms [J]. IEEE Trans Med Imag, 1996, 15(2): 218-229.
[32] Tao D, Li X, Wu X, et al. Supervised tensor learning [J]. J Knowl Inf Syst, 2007, 13(1): 1-42.
[33] Tao D, Li X, Wu X, et al. General Tensor discriminant analysis and gabor Features for gait recognition [J]. IEEE Trans Pattern Anal Mach Intell, 2007, 29(10): 1700-1715.
[34] Tsujii O, Freedman M T, Mun S K. Classification of microcalcifications in digital mammograms using trend-oriented radial basis function neural network [J]. Pattern Recognition, 1999, 32(5): 891-903.
[35] Verma B, Zakos J. A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques [J]. IEEE Trans Inf Technol Biomed, 2001, 5(1): 46-54.
[36] Wang T, Karayiannis N. Detection of microcalcifications in digital mammograms using wavelets [J]. IEEE Trans Med Imag, 1998, 17(4): 498-509.
[37] Wei L, Yang Y, Nishikawa R M, et al. Relevance vector machine for automatic detection of clustered microcalcifications [J]. IEEE Trans Med Imag, 2005, 24(10): 1278-1285.
[38] Yoshida H, Doi K, Nishikawa R M, et al. An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms [J]. Academic Radiology, 1996, 3(8): 621-627.
[39] Yu S-N, Li K-Y, Huang Y-K. Detection of microcalcifications in digitalmammograms using wavelet filter and Markov random field model [J]. Computerized Medical Imaging and Graphics, 2006, 30(3): 163-173.
[40] Zhang L, Sankar R, Qian W. Advances in micro-calcification clusters detection in mammography [J]. Computers in Biology and Medicine, 2002, 32(6): 515-528.
[41] Zhang W, Doi K, Giger M L, et al. Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network [J]. Medical Physics, 1994, 21(4): 517-524.
[42] Zheng B, Qian W, Clarke L P. Digital mammography: mixed feature neural network with spectral entropy decision for detection of microcalcifications [J]. IEEE Trans Med Imag, 1996, 15(5): 589-597.
[1] Bazaraa M, Sherali H, Shetty C. Nonlinear programming: theory and algorithms [M]. 3rd ed. Malden: Wiley-interscience, 2006.
[2] Bertsekas D, Homer M, Logan D, et al. Nonlinear programming [M]. Nashua, NH: Athena scientific, 1995.
[3] Cai D, He X, Han J, Subspace learning based on tensor analysis(UIUCDCS-R- 2005-2572) [R]: Dept. of Computer Science Tech., Univ. of Illinois at Urbana-Champaign, 2005.
[4] El-Naqa I, Yongyi Y, Wernick M N, et al. A support vector machine approach for detection of microcalcifications [J]. IEEE Trans Med Imag, 2002, 21(12): 1552-1563.
[5] Fu Y, Huang T S. Image classification using correlation tensor analysis [J]. IEEE Trans Image Process, 2008, 17(2): 226-234.
[6] Fu Y, S. H T. Image Classification using correlation tensor analysis [J]. IEEE Trans Image Process, 2008, 17(2): 226-234.
[7] Itskov M. Tensor algebra and tensor analysis for engineers: with applications to continuum mechanics [M]. 2nd ed. Heidelberg: Springer Verlag, 2007.
[8] Jayadeva R, Chandra S. Twin support vector machines for pattern classification [J]. IEEE Trans Pattern Anal Mach Intell, 2007, 905-910.
[9] Lepore N, Brun C, Yi-Yu C, et al. Generalized tensor-based morphometry of HIV/AIDS using multivariatestatistics on deformation tensors [J]. IEEE Trans Med Imag, 2008, 27(1): 129-141.
[10] Li J, Zhang L, Tao D, et al. A Prior Neurophysiologic knowledge free tensor-Based scheme for single trial EEG classification [J]. IEEE Trans Neural Syst Rehabil Eng, 2009, 17(2): 107-115.
[11] Li X, S. L, Yan S, et al. Discriminant locally linear embedding With high-order tensor data [J]. IEEE Trans Syst, Man, Cybern B, Cybern, 2008, 38(2): 342-352.
[12] Lu H, Plataniotis K N, Venetsanopoulos A N. MPCA: Multilinear principal component analysis of tensor objects [J]. IEEE Trans Neural Netw, 2008, 19(1): 18-39.
[13] N. R, S. B. Dimensionality reduction based on tensor modeling for classification methods [J]. IEEE Trans Geosci Remote Sens, 2009, 47(4): 1123-1131.
[14] Pattichis M S, Bovik A C. Analyzing image structure by multidimensional frequency modulation [J]. IEEE Trans Pattern Anal Mach Intell, 2007, 29(5): 753-766.
[15] Rose C, Turi D, Williams A, et al. Web Services for the DDSM and digital mammography research [M]. Digital Mammography. 2006: 376-383.
[16] Sung Won P, Savvides M. Individual Kernel Tensor-Subspaces for Robust Face Recognition: A computationally efficient tensor framework without requiring mode factorization [J]. IEEE Trans Syst, Man, Cybern B, Cybern, 2007, 37(5): 1156-1166.
[17] Tao D, Li X, Wu X, et al. Supervised tensor learning [J]. J Knowl Inf Syst, 2007, 13(1): 1-42.
[18] Tao D, Li X, Wu X, et al. General tensor discriminant analysis and gabor features for gait recognition [J]. IEEE Trans Pattern Anal Mach Intell, 2007, 29(10): 1700-1715.
[19] Vasilescu M A O, Terzopoulos D. Multilinear (Tensor) ICA and dimensionality reduction[C]. 2007: 818-826.
[20] Wang H, Yan S, Huang T, et al. A convergent solution to tensor subspace learning[C]. in Proceedings of the International Joint Conference on Artificial Intelligence(IJCAI-07), 2007: 629-634.
[21] Wei L, Yang Y, Nishikawa R M, et al. A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications [J]. IEEE Trans Med Imag, 2005, 24(3): 371-380.
[22] Wei L, Yang Y, Nishikawa R M, et al. Relevance vector machine for automatic detection of clustered microcalcifications [J]. IEEE Trans Med Imag, 2005, 24(10): 1278-1285.
[23] Wu F, Liu Y, Zhuang Y. Tensor-based transductive learning for multimodality video semantic concept detection [J]. IEEE Trans Multimedia, 2009, 11(5): 868-878.
[24] Xiaofei He, Deng Cai, Niyogi P. Tensor subspace analysis[C]. in Proceedings of the Advances in Neural Information Processing Systems( NIPS2005), 2005: 499-506.
[25] Xu D, Yan S, Tao D, et al. Human gait recognition with matrix representation [J]. IEEE Trans Circuits Syst Video Technol, 2006, 16(7): 896-903.
[26] Xu D, Yan S, Zhang L, et al. Reconstruction and recognition of tensor-based objects with concurrent subspaces analysis [J]. IEEE Trans Circuits Syst Video Technol, 2008, 18(1): 36-47.
[27] Yan S, Xu D, Yang Q, et al. Discriminant analysis with tensor representation [M].IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). IEEE Computer Society. 2005: 526-532.
[28] Yan S, Xu D, Yang Q, et al. Multilinear discriminant analysis for face recognition [J]. IEEE Trans Image Process, 2007, 16(1): 212-220.
[29] Zhang E, Yeh H, Zhongzang L, et al. Asymmetric tensor analysis for flow visualization [J]. IEEE Trans Vis Comput Graphics, 2009, 15(1): 106-122.
[1] Bocchi L, Coppini G, Nori J, et al. Detection of single and clustered micro calcifications in mammograms using fractals models and neural networks [J]. Medical Engineering & Physics, 2004, 26(4): 303-312.
[2] Boyd J P. Chebyshev and fourier spectral methods [M]. North Chelmsfo: Courier Dover Publications, 2001.
[3] Breiman L. Bagging Predictors [J]. Machine Learning, 1996, 24(2): 123-140.
[4] Cheng H-D, Lui Y M, Freimanis R I. A novel approach to micro calcification detection using fuzzy logic technique [J]. IEEE Trans Med Imag, 1998, 17(3): 442-450.
[5] Cheng H D, Cai X, Chen X, et al. Computer-aided detection and classification of micro calcifications in mammograms: a survey [J]. Pattern Recognition, 2003, 36(12): 2967-2991.
[6] Cheng H D, Wang J, Shi X. Micro calcification detection using fuzzy logic and scale space approaches [J]. Pattern Recognition, 2004, 37(2): 363-375.
[7] Department T D, Dietterich T G. Machine learning research: four current directions [J]. AI Magazine, 1997, 18(1): 97-136.
[8] Duda R O, Hart P E, Stork D G. Pattern classification [M]. 2nd ed. New York: John Wiley & Sons, Inc., 2001.
[9] El-Naqa I, Yongyi Y, Wernick M N, et al. A support vector machine approach for detection of micro calcifications [J]. IEEE Trans Med Imag, 2002, 21(12): 1552-1563.
[10] Elter M, Horsch A. CADx of mammographic masses and clustered microcalcifications: Areview [J]. Medical Physics, 2009, 36(6): 2052-2068.
[11] Fu J C, Lee S K, Wong S T C, et al. Image segmentation feature selection and pattern classification for mammographic microcalcifications [J]. Computerized Medical Imaging and Graphics, 2005, 29(6): 419-429.
[12] Gavrielides M A, Lo J Y, Floyd J C E. Parameter optimization of a computer-aided diagnosis scheme for the segmentation of micro calcificationclusters in mammograms [J]. Medical Physics, 2002, 29(4): 475-483.
[13] Ghazavi S N, Liao T W. Medical data mining by fuzzy modeling with selected features [J]. Artificial Intelligence inMedicine, 2008, 43(3): 195-206.
[14] Halkiotis S, Botsis T, Rangoussi M. Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks [J]. Signal Processing, 2007, 87(7): 1559-1568.
[15] Heng-Da C, Yui Man L, Freimanis R I. A novel approach to micro calcification detection using fuzzy logic technique [J]. IEEE Trans Med Imag, 1998, 17(3): 442-450.
[16] Jesneck J L, Nolte L W, Baker J A, et al. Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis [J]. Medical Physics, 2006, 33(8): 2945-2954.
[17] Kocur C M, Rogers S K, Myers L R, et al. Using neural networks to select wavelet features for breast cancer diagnosis [J]. IEEE Eng Med Biol Mag, 1996, 15(3): 95-102, 108.
[18] Krogh A, Vedelsby J. Neural network ensembles, cross validation, and active learning [J]. Advances in neural information processing systems(NIPS 1995), 1995,231-238.
[19] Ming L, Zhi-Hua Z. Improve Computer-aided diagnosis with machine learning techniques using undiagnosed samples [J]. IEEE Trans Syst, Man, Cybern A, 2007,37(6): 1088-1098.
[20] Pal N R, Bhowmick B, Patel S K, et al. A multi-stage neural network aided system for detection of microcalcifications in digitized mammograms [J]. Neurocomputing, 2008, 71(13-15): 2625-2634.
[21] Papadopoulos A, Fotiadis D I, Likas A. Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines [J]. Artificial Intelligence inMedicine, 2005, 34(2): 141-150.
[22] Retico A, Delogu P, Fantacci M E, et al. A scalable computer-aided detection system for micro calcification cluster identification in a pan-European distributed database of mammograms [J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2006, 569(2): 601-605.
[23] Schapire R, Singer Y. Improved boosting algorithms using confidence-rated predictions [J]. Machine Learning, 1999, 37(3): 297-336.
[24] Schapire R E. The strength of weak learnability [J]. Machine Learning, 1990, 5(2): 197-227.
[25] Tamura H, Mori S, Yamawaki T. Textural features corresponding to visual perception [J]. IEEE Trans Syst, Man, Cybern, 1978, 8(6): 460-473.
[26] Tsong-Wuu L, Yun-Feng C. A comparative study of Zernike moments [C]. inProceedings of the IEEE/WIC International Conference on Web Intelligence, 2003:516-519.
[27] Veldkamp W J H, Karssemeijer N, Otten J D M, et al. Automated classificationof clustered micro calcifications into malignant and benign types [J]. Medical Physics, 2000, 27(11): 2600-2608.
[28] Verma B, Zhang P. A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms [J]. Applied Soft Computing, 2007, 7(2): 612-625.
[29] Wei L, Yang Y, Nishikawa R M, et al. A study on several machine-learning methods for classification of malignant and benign clustered micro calcifications [J]. IEEE Trans Med Imag, 2005, 24(3): 371-380.
[30] Wei L, Yang Y, Nishikawa R M, et al. Relevance vector machine for automatic detection of clustered micro calcifications [J]. IEEE Trans Med Imag, 2005, 24(10): 1278-1285.
[31] Yu S, Guan L. A CAD system for the automatic detection of clustered micro calcifications in digitized mammogram films [J]. IEEE Trans Med Imag, 2000, 19(2): 115-126.
[1] Agrafiotis D. Stochastic proximity embedding [J]. Journal of computational chemistry, 2003, 24(10): 1215-1221.
[2] Baudat G, Anouar F. Generalized discriminant analysis using a kernel approach [J]. Neural computation, 2000, 12(10): 2385-2404.
[3] Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation [J]. Neural computation, 2003, 15(6): 1373-1396.
[4] Brand M. Charting a manifold[C]. in Proceedings of the Advances in Neural Information Processing Systems(NIPS 2003), 2003: 985-992.
[5] Cox T F, Cox M A A. Multidimensional scaling [M]. 2nd ed. London: CRC Press, 2000.
[6] De Santo M, Molinara M, Tortorella F, et al. Automatic classification of clustered microcalcifications by a multiple expert system [J]. Pattern Recognition, 2003, 36(7): 1467-1477.
[7] DeMers D, Cottrell G. Non-linear dimensionality reduction[C]. in Proceedings ofthe Advances in neural information processing systems(NIPS 1993), 1993: 580-587.
[8] Donoho D, Grimes C. Hessian eigenmaps: locally linear embedding techniques for high-dimensional data [J]. Proceedings of the National Academy of Sciences, 2003, 100(10): 5591-5596.
[9] Friedman J. Exploratory projection pursuit [J]. Journal of the American statistical association, 1987, 249-266.
[10] Globerson A, Roweis S. Visualizing pairwise similarity via semidefinite embedding[C]. in Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2007: 50-58.
[11] Hall D L, Llinas J. Handbook of multisensor data fusion [M]. London: CRC Press, 2001.
[12] Harman H. Modern factor analysis [M]. Chicago: University of Chicago Press, 1976.
[13] He X, Cai D, Yan S, et al. Neighborhood preserving embedding[C]. in Proceedings of the Tenth IEEE International Conference on Computer Vision(ICCV 2005), 2005: 1208-1213.
[14] He X, Niyogi P. Locality preserving projections[C]. in Proceedings of the Advances in neural information processing systems(NIPS 2003), 2003: 153-160.
[15] Hinton G, Roweis S. Stochastic neighbor embedding[C]. in Proceedings of the Advances in neural information processing systems(NIPS 2003), 2003: 857-864.
[16] Jesneck J L, Nolte L W, Baker J A, et al. Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis [J]. Medical Physics, 2006, 33(8): 2945-2954.
[17] Jolliffe I T. Principal component analysis [M]. 2nd ed. New York: Springer verlag, 2002.
[18] Lafon S, Lee A. Diffusion maps and coarse-graining: A unified framework for dimensionality reduction, graph partitioning, and data set parameterization [J]. IEEE Trans Pattern Anal Mach Intell, 2006, 28(9): 1393-1403.
[19] Li M, Zhou Z-H. Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples [J]. IEEE Trans Syst, Man, Cybern A, Syst, Humans, 2007, 37(6): 1088-1098.
[20] Maaten L J P, Hinton G E. Visualizing data using t-SNE [J]. Journal of Machine Learning Research, 2008, 9(11): 2431–2456.
[21] Nadler B, Lafon S, Coifman R, et al. Diffusion maps, spectral clustering and reaction coordinates of dynamical systems [J]. Appl Comput Harmon Anal, 2006, 21(1): 113-127.
[22] Psychol J E, Generalis A, Genet S, et al. The use of multiple measurements in taxonomic problems [J]. Ann of Eugenics, 1936, 7(1): 179 - 188.
[23] Roweis S, Saul L. Nonlinear dimensionality reduction by locally linear embedding [J]. Science, 2000, 290(5500): 2323-2326.
[24] Roweis S, Saul L. Global coordination of local linear models[C]. in Proceedings of the Advances in neural information processing systems(NIPS 2002), 2002: 889.
[25] Scholkopf B, Smola A, Muller K. Nonlinear component analysis as a kernel eigenvalue problem [J]. Neural computation, 1998, 10(5): 1299-1319.
[26] Sha F, Saul L. Analysis and extension of spectral methods for nonlinear dimensionality reduction[C]. in Proceedings of the 22nd International Conference on Machine Learning, 2005: 785-792.
[27] Tenenbaum J B. Mapping a manifold of perceptual observations[C]. in Proceedings of the Neural Information Processing Systems(NIPS 1998), 1998: 682-688.
[28] Verbeek J. Learning nonlinear image manifolds by global alignment of local linear models [J]. IEEE Trans Pattern Anal Mach Intell, 2006, 28(8): 1236-1250.
[29] Weinberger K, Saul L. An introduction to nonlinear dimensionality reduction by maximum variance unfolding[C]. in Proceedings of the Twenty-first National Conference on Artificial Intelligence (AAAI-06), 2006: 1683-1686.
[30] Zhang T, Yang J, Zhao D, et al. Linear local tangent space alignment and application to face recognition [J]. Neurocomputing, 2007, 70(7-9): 1547-1553.
[31] Zhang Z, Zha H. Principal manifolds and nonlinear dimension reduction via local tangent space alignment [J]. SIAM J Scientific Computing, 2004, 26(1): 313-338.
[32] Zhou Z, Wu J, Tang W. Ensembling neural networks: many could be better than all [J]. Artificial Intelligence, 2002, 137(1): 239-263.
[2] Bagci A M, Cetin A E. Detection of microcalcifications in mammograms using local maxima and adaptive wavelet transform analysis [J]. Electronics Letters, 2002, 38(22): 1311-1313.
[3] Bagci A M, Yardimci Y, Cetin A E. Detection of microcalcification clusters in mammogram images using local maxima and adaptive wavelet transform analysis[C]. in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP 2002), 2002: 3856-3859.
[4] Ben Hamad N, Taouil K. Exploring wavelets subband decomposition toward a computer aided detection of micro calcification in breast cancer[C]. in Proceedings of the 2nd International Conference on Distributed Frameworks for Multimedia Applications, 2007: 229-236.
[5] Bocchi L, Nori J. Shape analysis of microcalcifications using Radon transform [J]. Medical Engineering & Physics, 2007, 29(6): 691-698.
[6] Boccignone G, Chianese A, Picariello A. Computer aided detection of microcalcifications in digital mammograms [J]. Computers in Biology andMedicine, 2000, 30(5): 267-286.
[7] Cahoon T C, Sutton M A, Bezdek J C. Breast cancer detection using image processing techniques[C]. in Proceedings of the IEEE International Conference on Fuzzy Systems, 2000: 973-976.
[8] Chan H-P, Sahiner B, Lam K L, et al. Computerized analysis of mammographic microcalcifications in morphological and texture feature spaces [J]. Medical Physics, 1998, 25(10): 2007-2019.
[9] Chan H P, Doi K, Vybrony C J, et al. Improvement in Radiologists' Detection of Clustered Microcalcifications on Mammograms: The Potential of Computer-Aided Diagnosis [J]. Investigative Radiology, 1990, 25(10): 1102-1110.
[10] Chao W, Wei J, Xifeng D. Characterization of clustered microcalcifications in mammograms based on support vector machines with genetic algorithms[C]. in Proceedings of the International Symposium on Biophotonics, Nanophotonics and Metamaterials, Metamaterials, 2006: 114-117.
[11] Cheng H-D, Lui Y M, Freimanis R I. A novel approach to microcalcification detection using fuzzy logic technique [J]. IEEE Trans Med Imag, 1998, 17(3): 442-450.
[12] Cheng H D, Cai X, Chen X, et al. Computer-aided detection and classification of microcalcifications in mammograms: a survey [J]. Pattern Recognition, 2003, 36(12): 2967-2991.
[13] Cheng H D, Chen J R, Freimanis R I, et al. A novel fuzzy logic approach to microcalcification detection [J]. Information Sciences, 1998, 111(1-4): 189-205.
[14] Cheng H D, Wang J, Shi X. Microcalcification detection using fuzzy logic and scale space approaches [J]. Pattern Recognition, 2004, 37(2): 363-375.
[15] Cortes C, Vapnik V. Support-vector networks [J]. Machine learning, 1995, 20(3): 273-297.
[16] Davies D H, Dance D R. Automatic computer detection of clustered calcifications in digital mammograms [J]. Physics in Medicine & Biology, 1990, 35(8): 1111-1118.
[17] De Santo M, Molinara M, Tortorella F, et al. Automatic classification of clustered microcalcifications by a multiple expert system [J]. Pattern Recognition, 2003, 36(7): 1467-1477.
[18] Dengler J, Behrens S, Desaga J F. Segmentation of microcalcifications in mammograms [J]. IEEE Trans Med Imag, 1993, 12(4): 634-642.
[19] Dhawan A P, Chitre Y, Kaiser-Bonasso C. Analysis of mammographic microcalcifications using gray-level image structure features [J]. IEEE Trans Med Imag, 1996, 15(3): 246-259.
[20] Doi K. Computer-aided diagnosis in medical imaging: historical review, current status and future potential [J]. Computerized Medical Imaging and Graphics, 2007, 31(4-5): 198-211.
[21] El-Naqa I, Yongyi Y, Wernick M N, et al. A support vector machine approach fordetection of microcalcifications [J]. IEEE Trans Med Imag, 2002, 21(12): 1552-1563.
[22] Elter M, Horsch A. CADx of mammographic masses and clustered microcalcifications: A review [J]. Medical Physics, 2009, 36(6): 2052-2068.
[23] Ema T, Doi K, Nishikawa R M, et al. Image feature analysis and computer-aided diagnosis in mammography: Reduction of false-positive clustered microcalcifications using local edge-gradient analysis [J]. Medical Physics, 1995, 22(2): 161-169.
[24] Fam B W, Olson S L, Winter P F, et al. Algorithm for the detection of fine clustered calcifications on film mammograms [J]. Radiology, 1988, 169(2): 333-337.
[25] Fu J C, Lee S K, Wong S T C, et al. Image segmentation feature selection and pattern classification for mammographic microcalcifications [J]. Computerized Medical Imaging and Graphics, 2005, 29(6): 419-429.
[26] Garcia-Orellana C J, Gallardo-Caballero R, Macias-Macias M, et al. SVM and Neural Networks comparison in mammographic CAD[C]. in Proceedings of the 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, 2007: 3204-3207.
[27] Gavrielides M A, Lo J Y, Floyd J C E. Parameter optimization of a computer-aided diagnosis scheme for the segmentation of microcalcification clusters in mammograms [J]. Medical Physics, 2002, 29(4): 475-483.
[28] Gavrielides M A, Lo J Y, Vargas-Voracek R, et al. Segmentation of suspicious clustered microcalcifications in mammograms [J]. Medical Physics, 2000, 27(1): 13-22.
[29] Gunawan D. Microcalcification detection using wavelet transform[C]. in Proceedings of the IEEE Pacific RIM Conference on Communications, Computers, and Signal Processing, 2001: 694-697.
[30] Gurcan M N, Chan H-P, Sahiner B, et al. Optimal Neural Network Architecture Selection: Improvement in Computerized Detection of Microcalcifications [J]. Academic Radiology, 2002, 9(4): 420-429.
[31] Halkiotis S, Botsis T, Rangoussi M. Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks [J]. Signal Processing, 2007, 87(7): 1559-1568.
[32] Heath M, Bowyer K, Kopans D, et al. The digital database for screening mammography [J]. Int Work on Dig Mammography, 2000, 212–218.
[33] Highnam R, Brady M. Mammographic image analysis [M]. Dordrecht: Kluwer Academic Pub, 1999.
[34] Hojjatoleslami A, Sardo L, Kittler J. RBF based classifier for the detection of microcalcifications in mammograms with outlier rejection capability[C]. in Proceedings of the IEEE International Conference on Neural Networks, 1997: 1379-1384.
[35] Huang Y-K, Yu S-N. Recognition of microcalcifications in digital mammogramsbased on Markov random field and deterministic fractal modeling[C]. in Proceedings of the 29th Annual International Conference of IEEE-EMBS, Engineering in Medicine and Biology Society, 2007: 3922-3925.
[36] iCAD website. http://www.icadmed.com/ [M]. 2008.
[37] Jamarani S M H, Rezai-rad G, Behnam H. A novel method for breast cancer prognosis using wavelet packet based neural network[C]. in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology, 2005: 3414-3417.
[38] Jayadeva R, Chandra S. Twin Support Vector Machines for Pattern Classification [J]. IEEE Trans Pattern Anal Mach Intell, 2007, 905-910.
[39] Jesneck J L, Nolte L W, Baker J A, et al. Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis [J]. Medical Physics, 2006, 33(8): 2945-2954.
[40] Jiang J, Yao B, Wason A M. Integration of fuzzy logic and structure tensor towards mammogram contrast enhancement [J]. Computerized Medical Imaging and Graphics, 2005, 29(1): 83-90.
[41] Jiang Y, Nishikawa R M, Papaioannou J. Dependence of computer classification of clustered microcalcifications on the correct detection of microcalcifications [J]. Medical Physics, 2001, 28(9): 1949-1957.
[42] Juarez L C, Ponomaryov V, Sanchez J L R. Detection of microcalcifications in digital mammograms images using wavelet transform[C]. in Proceedings of the Electronics, Robotics and Automotive Mechanics Conference, 2006: 58-61.
[43] Kahn E, Gavoille A, Masselot J. Computer analysis of breast calcifications in mammographic images[C]. in Proceedings of the Proceedings of the International Symposium on Computer Assisted Radiology, 1987: 729-733.
[44] Kallergi M. Computer-aided diagnosis of mammographic microcalcification clusters [J]. Medical Physics, 2004, 31(2): 314-326.
[45] Karssemeijer N. Stochastic model for automated detection of calcifications in digital mammograms [J]. Image and Vision Computing, 1992, 10(6): 369-375.
[46] Karssemeijer N. Adaptive Noise Equalization and Recognition of Microcalcification Clusters in Mammograms [J]. International Journal of Pattern Recognition and Artificial Intelligence, 1993, 7(6): 1357-1376.
[47] Kim J K, Park H W. Surrounding region dependence method for detection of clustered microcalcifications on mammograms[C]. in Proceedings of the IEEE International Conference on Image Processing, 1997: 535-538.
[48] Kocur C M, Rogers S K, Myers L R, et al. Using neural networks to select wavelet features for breast cancer diagnosis [J]. IEEE Eng Med Biol Mag, 1996, 15(3): 95-102, 108.
[49] Kopans D B. Breast imaging [M]. 3rd ed. New York: Lippincott Williams & Wilkins, 2006.
[50] Lassetn C, Bonadona V. Medical management of women with inherited predisposition to breast cancer: indications and procedures for mammographicscreening [J]. Bull Cancer, 2001, 88(7): 677.
[51] Le Gal M, Chavanne G, Pellier D. Diagnostic value of clustered microcalcifications discovered by mammography (apropos of 227 cases with histological verification and without a palpable breast tumor) [J]. Bulletin du cancer, 1984, 71(1): 57-57.
[52] Lefebvre F, Benali H, Gilles R, et al. A fractal approach to the segmentation of microcalcifications in digital mammograms [J]. Medical Physics, 1995, 22(4): 381-390.
[53] Lei Z, Xieping G. Research on translation-invariant wavelet transform for classification in mammograms[C]. in Proceedings of the Third International Conference on Natural Computation, 2007: 571-575.
[54] Li M, Zhou Z-H. Improve Computer-Aided Diagnosis With Machine Learning Techniques Using Undiagnosed Samples [J]. IEEE Trans Syst, Man, Cybern A, Syst, Humans, 2007, 37(6): 1088-1098.
[55] Lim K J, Choi C S, Yoon D Y, et al. Computer-Aided Diagnosis for the Differentiation of Malignant from Benign Thyroid Nodules on Ultrasonography [J]. Academic Radiology, 2008, 15(7): 853-858.
[56] Lo S C B, Li H, Freedman M T. Optimization of wavelet decomposition for image compression and feature preservation [J]. IEEE Trans Med Imag, 2003, 22(9): 1141-1151.
[57] Lopez-Aligue F J, Acevedo-Sotoca I, Garcia-Manso A, et al. Microcalcifications detection in digital mammograms[C]. in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology, 2004: 1577-1580.
[58] Mascio L N. Automated analysis for microcalcifications in high resolution digital mammograms[M]. US Patent 5586160, 1996.
[59] Mencattini A, Caselli F, Salmeri M, et al. Wavelet based adaptive algorithm for mammographic images enhancement and denoising[C]. in Proceedings of the International Conference on Image Processing(ICIP 2005), 2005: 1141-1144.
[60] Mencattini A, Salmeri M, Lojacono R, et al. Mammographic images enhancement and denoising for microcalcification detection using dyadic wavelet processing[C]. in Proceedings of the IEEE Instrumentation and Measurement Technology Conference, 2006: 49-53.
[61] Mencattini A, Salmeri M, Lojacono R, et al. Mammographic Images Enhancement and Denoising for Breast Cancer Detection Using Dyadic Wavelet Processing [J]. IEEE Trans Instrumentation and Measurement, 2008, 57(7): 1422-1430.
[62] Mousa R, Munib Q, Moussa A. Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural [J]. Expert Systems with Applications, 2005, 28(4): 713-723.
[63] Nakayama R, Uchiyama Y, Yamamoto K, et al. Computer-aided diagnosis scheme using a filter bank for detection of microcalcification clusters in mammograms [J]. IEEE Trans Biomed Eng, 2006, 53(2): 273-283.
[64] National Cancer Institute. Breast Cancer Screening (http://www.cancer.gov/ cancertopics/pdq/screening/breast/Patient/page3) [M]. 2008.
[65] Nishikawa R M. Current status and future directions of computer-aided diagnosis in mammography [J]. Computerized Medical Imaging and Graphics, 2007, 31(4-5): 224-235.
[66] Pal N R, Bhowmick B, Patel S K, et al. A multi-stage neural network aided system for detection of microcalcifications in digitized mammograms [J]. Neurocomputing, 2008, 71(13-15): 2625-2634.
[67] Papadopoulos A, Fotiadis D I, Costaridou L. Improvement of microcalcification cluster detection in mammography utilizing image enhancement techniques [J]. Computers in Biology and Medicine, 2008, 38(10): 1045-1055.
[68] Papadopoulos A, Fotiadis D I, Likas A. An automatic microcalcification detection system based on a hybrid neural network classifier [J]. Artificial Intelligence in Medicine, 2002, 25(2): 149-167.
[69] Papadopoulos A, Fotiadis D I, Likas A. Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines [J]. Artificial Intelligence in Medicine, 2005, 34(2): 141-150.
[70] Peng Y, Huang Q, Jiang P, et al. Cost-sensitive ensemble of support vector machines for effective detection of microcalcification in breast cancer diagnosis[C]. 2005: 483-493.
[71] Qian W, Kallergi M, Clarke L P, et al. Tree structured wavelet transform segmentation of microcalcifications in digital mammography [J]. Medical Physics, 1995, 22(8): 1247-1254.
[72] R2 Technology website. http://www.r2tech.com/ [M]. 2009.
[73] Rangayyan R M, Ayres F J, Leo Desautels J E. A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs [J]. Journal of the Franklin Institute, 2007, 344(3-4): 312-348.
[74] Regentova E, Lei Z, Jun Z, et al. Detecting microcalcifications in digital mammograms using wavelet domain hidden Markov Tree Model[C]. in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology, 2006: 1972-1975.
[75] Regentova E, Zhang L, Zheng J, et al. Microcalcification detection based on wavelet domain hidden Markov tree model: Study for inclusion to computer aided diagnostic prompting system [J]. Medical Physics, 2007, 34(6): 2206-2219.
[76] Retico A, Delogu P, Fantacci M E, et al. A scalable computer-aided detection system for microcalcification cluster identification in a pan-European distributed database of mammograms [J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2006, 569(2): 601-605.
[77] Rezai-Rad G, Jamarani S. Detecting microcalcification clusters in digital mammograms using combination of wavelet and neural network[C]. in Proceedings of the Conference on Computer Graphics, Imaging and Vision: NewTrends 2005, 2005: 197-201.
[78] Riyahi-Alam N, Ahmadian A, Tehrani J N, et al. Segmentation of suspicious clustered microcalcifications on digital mammograms: Using fuzzy logic and wavelet coefficients[C]. in Proceedings of the 7th International Conference on Signal Processing, 2004: 2228-2230.
[79] Riyahi-Alam N, Ahmadian A, Tehrani J N, et al. Segmentation of suspicious clustered microcalcifications on digital mammograms: Using Fuzzy logic and wavelet coefficients[C]. 2004: 2226-2228.
[80] Sajda P, Spence C, Pearson J. Learning contextual relationships in mammograms using a hierarchical pyramid neural network [J]. IEEE Trans Med Imag, 2002, 21(3): 239-250.
[81] Salvado J, Roque B. Detection of calcifications in digital mammograms using wavelet analysis and contrast enhancement[C]. in Proceedings of the IEEE International Workshop on Intelligent Signal Processing, 2005: 200-205.
[82] Sampat M P, Markey M K, Bovik A C. Computer-aided detection and diagnosis in mammography [J]. Handbook of Image and Video Processing, 2005, 10(4): 1195-1217.
[83] Schapire R, Freund Y. A decision theoretic generalization of on-line learning and an application to boosting [J]. Journal of Computer System Sciences, 1997, 55(1): 119-139.
[84] Sehad S, Desarnaud S, Strauss A. Artificial neural classification of clustered microcalcifications on digitized mammograms[C]. in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 1997: 4217-4222.
[85] Shen L, Rangayyan R M, Desautels J E L. Application of shape analysis to mammographic calcifications [J]. IEEE Trans Med Imag, 1994, 13(2): 263-274.
[86] Song L, Wang Q, Gao J. Microcalcification detection using combination of wavelet transform and morphology[C]. in Proceedings of the International Conference on Signal Processing Proceedings, 2007: 16-20.
[87] Sorantin E, Schmidt F, Mayer H, et al. Computer aided diagnosis of clustered microcalcifications using artificial neural nets [J]. Journal of Computing and Information Technology, 2000, 8(2): 151-160.
[88] Spiesberger W, Forschungslaboratorium P G H. Mammogram inspection by computer [J]. IEEE Transactions on Biomedical Engineering, 1979, BME-26(4): 213-219.
[89] Strickland R N, Hahn H I. Wavelet transforms for detecting microcalcifications in mammograms [J]. IEEE Trans Med Imag, 1996, 15(2): 218-229.
[90] Strickland R N, Lukins G J. Fuzzy system improves the performance of wavelet-based correlation detectors[C]. in Proceedings of the IEEE International Conference on Image Processing, 1997: 404-407.
[91] Taylor P, Champness J, Given-Wilson R, et al. Impact of computer-aided detection prompts on the sensitivity and specificity of screening mammography [J]. Health Technology Assessment, 2005, 9(6): 1-70.
[92] Thangavel K, Karnan M, Sivakumar R, et al. Automatic Detection of Microcalcifications in Mammograms-A Review [J]. ICGST International Journal on Graphics, Vision and Image Processing, 2005, 5(5): 31-61.
[93] Tipping M E. Sparse Bayesian Learning and the Relevance Vector Machine [J]. Journal of Machine Learning Research, 2001, 1(3): 211-244.
[94] Tsujii O, Freedman M T, Mun S K. Classification of microcalcifications in digital mammograms using trend-oriented radial basis function neural network [J]. Pattern Recognition, 1999, 32(5): 891-903.
[95] Veldkamp W J H, Karssemeijer N, Otten J D M, et al. Automated classification of clustered microcalcifications into malignant and benign types [J]. Medical Physics, 2000, 27(11): 2600-2608.
[96] Verma B. Neural network based technique to locate and classify microcalcifications in digital mammograms[C]. in Proceedings of the IEEE International Conference on Neural Networks, 1998: 1790-1793.
[97] Verma B, Zakos J. A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques [J]. IEEE Trans Inf Technol Biomed, 2001, 5(1): 46-54.
[98] Vibert J F, Valleron A J. Automatic detection of microcalcifications in mammography using a neuromimetic system based on retina [J]. Studies in health technology and informatics, 2003, 95(1): 589-594.
[99] Wang J, Cheng H D. Fuzzy logic and scale space approach to microcalcification detection[C]. in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP 2003), 2003: 345-348.
[100] Wang T, Karayiannis N. Detection of microcalcifications in digital mammograms using wavelets [J]. IEEE Trans Med Imag, 1998, 17(4): 498-509.
[101] Warren R. Screening women at high risk of breast cancer on the basis of evidence [J]. European Journal of Radiology, 2001, 39(1): 50-59.
[102] Wei L, Yang Y, Nishikawa R M. Retrieval-driven microcalcification classification for breast cancer diagnosis[C]. in Proceedings of the 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2007: 1260-1263.
[103] Wei L, Yang Y, Nishikawa R M. Microcalcification classification assisted by content-based image retrieval for breast cancer diagnosis [J]. Pattern Recognition, 2009, 42(6): 1126-1132.
[104] Wei L, Yang Y, Nishikawa R M, et al. A study on several Machine-learning methods for classification of Malignant and benign clustered microcalcifications [J]. IEEE Trans Med Imag, 2005, 24(3): 371-380.
[105] Wei L, Yang Y, Nishikawa R M, et al. Relevance vector machine for automatic detection of clustered microcalcifications [J]. IEEE Trans Med Imag, 2005, 24(10): 1278-1285.
[106] World Health Organization. Cancer (http://www.who.int/mediacentre/factsheets/ fs297/en/index.html) [M]. 2009.
[107] Yang T, Guo S, Wu X, et al. An approach based on immune algorithm and SVM for detection and classification of microcalcifications[C]. in Proceedings of the 1st International Conference on Bioinformatics and Biomedical Engineering, 2007: 588-591.
[108] Yoshida H, Doi K, Nishikawa R M, et al. An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms [J]. Academic Radiology, 1996, 3(8): 621-627.
[109] Yu S, Guan L. A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films [J]. IEEE Trans Med Imag, 2000, 19(2): 115-126.
[110] Zhang L, Qian W, Sankar R, et al. A new false positive reduction method for MCCs detection in digital mammography[C]. in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing(ICASSP 2001), 2001: 1033-1036.
[111] Zhang L, Sankar R, Qian W. Advances in micro-calcification clusters detection in mammography [J]. Computers in Biology and Medicine, 2002, 32(6): 515-528.
[112] Zhang W, Doi K, Giger M L, et al. Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network [J]. Medical Physics, 1994, 21(4): 517-524.
[113] Zhang X-P, Desai M D, Shah R B. Automatic wavelet-based detection of clustered microcalcifications[C]. in Proceedings of the Southern Biomedical Engineering Conference, 1998: 75-75.
[114] Zheng B, Qian W, Clarke L P. Digital mammography: mixed feature neural network with spectral entropy decision for detection of microcalcifications [J]. IEEE Trans Med Imag, 1996, 15(5): 589-597.
[115] Zolghadrasli A R, Maghsoodzadeh Z. Modification of mammograms for early diagnosis of breast cancer using wavelet and neural networks[C]. in Proceedings of the IEEE International Conference on Computer Systems and Applications, 2006: 288-295.
[116]万柏坤,王瑞平,朱欣.基于综合处理方法的乳腺X影像中微钙化点检测新技术[J].中国生物医学工程学报, 2002, 21(06): 57-63.
[117]万柏坤,王瑞平,朱欣, et al. SVM算法及其在乳腺X片微钙化点自动检测中的应用[J].电子学报, 2004, 32(04): 61-64.
[118]李树楠,万柏坤,马振鹤, et al.基于小波变换的乳腺X线影像微钙化点感兴趣区域提取新技术[J].生物医学工程学杂志, 2005, 22(02): 144-146.
[119]胡正平,张晔.基于可拒识-双层支持向量分类器的微钙化点检测[J].中国图象图形学报, 2006, 05): 58-61.
[120]胡正平,张晔.基于快速可拒识-双层支持向量分类器的微钙化点的检测算法[J].仪器仪表学报, 2007, 03): 64-68.
[121]胡正平,张晔.基于微弱目标-背景合成技术的微钙化点检测[J].中国生物医学工程学报, 2007, 04): 160-163.
[122]陈磊,张凯,刘克钦.基于模糊C均值和小波多分辨分析的数字乳房X射线片钙化点提取算法研究[J].生物医学工程学进展, 2009, 30(01): 20-22.
[123]马慧彬,张海燕,丁晓迪. Top-hat与SVM在乳腺微钙化点检测中的应用[J].计算机工程与应用, 2009, 45(20): 247-249.
[1] Bankman I N, Nizialek T, Simon I, et al. Segmentation algorithms for detecting microcalcifications in mammograms [J]. IEEE Trans Inf Technol Biomed, 1997, 1(2): 141-149.
[2] Baram Y, El-Yaniv R, Luz K. Online choice of active learning algorithms [J]. The Journal of Machine Learning Research, 2004, 5(1): 255-291.
[3] Boccignone G, Chianese A, Picariello A. Computer aided detection of microcalcifications in digital mammograms [J]. Computers in Biology and Medicine, 2000, 30(5): 267-286.
[4] Cohn D, Atlas L, Ladner R. Improving generalization with active learning [J]. Machine learning, 1994, 15(2): 201-221.
[5] Cohn D, Ghahramani Z, Jordan M. Active learning with statistical models [J]. Journal of artificial intelligence research, 1996, 129-145.
[6] Cooklev T, Nishihara A, Sablatash M. Regular orthonormal and biorthogonal wavelet filters [J]. Signal Processing, 1997, 57(2): 121-137.
[7] Daubechies I. Ten lectures on wavelets [M]. Philadelphia, PA: Society for Industrial Mathematics, 1992.
[8] Domingos P, Pazzani M. Beyond independence: Conditions for the optimality of the simple Bayesian classifier [J]. Machine learning, 1996, 24(3): 105-112.
[9] Domingos P, Pazzani M. On the optimality of the simple Bayesian classifier under zero-one loss [J]. Machine learning, 1997, 29(2): 103-130.
[10] Friedman N, Geiger D, Goldszmidt M. Bayesian network classifiers [J]. Machine learning, 1997, 29(2): 131-163.
[11] Halkiotis S, Botsis T, Rangoussi M. Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks [J]. Signal Processing, 2007, 87(7): 1559-1568.
[12] Jin H, Kobatake H. Extraction of microcalcifications from mammograms using morphological filter with multiple structuring elements [J]. Systems and Computers in Japan, 1993, 24(11): 66-74.
[13] Mallat S. A theory for multiresolution signal decomposition: The wavelet representation [J]. IEEE Trans Pattern Anal Mach Intell, 1989, 11(7): 674-693.
[14] McLoughlin K J, Bones P J, Karssemeijer N. Noise equalization for detection of microcalcification clusters in direct digital mammogram images [J]. IEEE Trans Med Imag, 2004, 23(3): 313-320.
[15] Nakayama R, Uchiyama Y, Yamamoto K, et al. Computer-aided diagnosis scheme using a filter bank for detection of microcalcification clusters in mammograms [J]. IEEE Trans Biomed Eng, 2006, 53(2): 273-283.
[16] Qian W, Kallergi M, Clarke L P, et al. Tree structured wavelet transform segmentation of microcalcifications in digital mammography [J]. Medical Physics, 1995, 22(8): 1247-1254.
[17] Shimizu A, Toriwaki J, Hasegawa J. Characteristics of rotatory second order difference filter for computer aided diagnosis of medical images [J]. Systems and Computers in Japan, 1995, 26(11): 38-51.
[18] Strickland R, Hahn H. Wavelet transforms for detecting microcalcifications in mammograms [J]. IEEE Trans Med Imag, 1996, 15(2): 218-229.
[19] Strickland R, Hahn H. Wavelet transform methods for object detection and recovery [J]. IEEE Trans Image Process, 1997, 6(5): 724-735.
[20] Strickland R N, Hahn H I. Wavelet transforms for detecting microcalcifications in mammograms [J]. IEEE Trans Med Imag, 1996, 15(2): 218-229.
[21] Tilbury J, Van Eetvelt W, Garibaldi J, et al. Receiver operating characteristic analysis for intelligent medical systems-a new approach for finding confidence intervals [J]. IEEE Trans Biomed Eng, 2000, 47(7): 952-963.
[22] Valiant L G. A theory of the learnable [J]. Communications of the ACM, 1984, 27(11): 1134-1142.
[23] Wang T, Karayiannis N. Detection of microcalcifications in digital mammograms using wavelets [J]. IEEE Trans Med Imag, 1998, 17(4): 498-509.
[24] Winston P, Learning Structural Descriptions from Examples (AITR-231) [R]. Cambridge, MA, USA: Massachusetts Institute of Technology, 1970.
[25] Yoshida H, Doi K, Nishikawa R, et al. An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms [J]. Academic Radiology, 1996, 3(8): 621.
[26] Yu S, Guan L. A CAD system for the automatic detection of clustered microcalcifications in digitized mammogram films [J]. IEEE Trans Med Imag, 2000, 19(2): 115-126.
[1] http://marathon.csee.usf.edu/Mammography/Database.html [M].
[2] Bagci A M, Cetin A E. Detection of microcalcifications in mammograms using local maxima and adaptive wavelet transform analysis [J]. Electronics Letters, 2002, 38(22): 1311-1313.
[3] Bagnall M J C, Evans A J, Wilson A R M, et al. Predicting Invasion in Mammographically Detected Microcalcification [J]. Clinical Radiology, 2001, 56(10): 828-832.
[4] Bocchi L, Coppini G, Nori J, et al. Detection of single and clustered microcalcifications in mammograms using fractals models and neural networks [J]. Medical Engineering & Physics, 2004, 26(4): 303-312.
[5] Cai D, He X, Han J, Subspace learning based on tensor analysis(UIUCDCS-R- 2005-2572) [R]: Dept. of Computer Science Tech., Univ. of Illinois at Urbana- Champaign, 2005.
[6] Chen C H, Lee G G. On Digital mammogram segmentation and microcalcification detection using multiresolution wavelet analysis [J]. Graphical Models and Image Processing, 1997, 59(5): 349-364.
[7] Cheng H-D, Lui Y M, Freimanis R I. A novel approach to microcalcification detection using fuzzy logic technique [J]. IEEE Trans Med Imag, 1998, 17(3): 442-450.
[8] Cheng H D, Cai X, Chen X, et al. Computer-aided detection and classification of microcalcifications in mammograms: a survey [J]. Pattern Recognition, 2003, 36(12): 2967-2991.
[9] Cheng H D, Chen J R, Freimanis R I, et al. A novel fuzzy logic approach to microcalcification detection [J]. Information Sciences, 1998, 111(1-4): 189-205.
[10] Cheng H D, Wang J, Shi X. Microcalcification detection using fuzzy logic and scale space approaches [J]. Pattern Recognition, 2004, 37(2): 363-375.
[11] El-Naqa I, Yongyi Y, Wernick M N, et al. A support vector machine approach for detection of microcalcifications [J]. IEEE Trans Med Imag, 2002, 21(12): 1552-1563.
[12] Fukunaga K. Introduction to Statistical Pattern Recognition [M]. 2nd ed. Boston: Academic Press, 1990.
[13] Gulsrud T O, Husoy J H. Optimal filter-based detection of microcalcifications [J]. IEEE Trans Biomed Eng, 2001, 48(11): 1272-1281.
[14] Gurcan M N, Chan H-P, Sahiner B, et al. Optimal Neural Network Architecture Selection: Improvement in Computerized Detection of Microcalcifications [J]. Academic Radiology, 2002, 9(4): 420-429.
[15] Halkiotis S, Botsis T, Rangoussi M. Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks [J]. Signal Processing, 2007, 87(7): 1559-1568.
[16] He X, Cai D, Niyogi P. Tensor Subspace Analysis[C]. in Proceedings of the Advances in Neural Information Processing Systems( NIPS 2005), 2005: 499-506.
[17] Jayadeva R, Chandra S. Twin support vector machines for pattern classification [J]. IEEE Trans Pattern Anal Mach Intell, 2007, 905-910.
[18] Jiang J, Yao B, Wason A M. Integration of fuzzy logic and structure tensor towards mammogram contrast enhancement [J]. Computerized Medical Imaging and Graphics, 2005, 29(1): 83-90.
[19] Kocur C M, Rogers S K, Myers L R, et al. Using neural networks to select wavelet features for breast cancer diagnosis [J]. IEEE Eng Med Biol Mag, 1996, 15(3): 95-102, 108.
[20] Kopans D B. Breast imaging [M]. 3rd ed. New York: Lippincott Williams & Wilkins, 2006.
[21] Mencattini A, Salmeri M, Lojacono R, et al. Mammographic Images Enhancement and Denoising for Breast Cancer Detection Using Dyadic Wavelet Processing [J]. IEEE Trans Instrumentation and Measurement, 2008, 57(7): 1422-1430.
[22] Mousa R, Munib Q, Moussa A. Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural [J]. Expert Systems with Applications, 2005, 28(4): 713-723.
[23] Nakayama R, Uchiyama Y, Yamamoto K, et al. Computer-aided diagnosis scheme using a filter bank for detection of microcalcification clusters in mammograms [J]. IEEE Trans Biomed Eng, 2006, 53(2): 273-283.
[24] Pal N R, Bhowmick B, Patel S K, et al. A multi-stage neural network aided system for detection of microcalcifications in digitized mammograms [J].Neurocomputing, 2008, 71(13-15): 2625-2634.
[25] Papadopoulos A, Fotiadis D I, Likas A. An automatic microcalcification detection system based on a hybrid neural network classifier [J]. Artificial Intelligence in Medicine, 2002, 25(2): 149-167.
[26] Papadopoulos A, Fotiadis D I, Likas A. Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines [J]. Artificial Intelligence in Medicine, 2005, 34(2): 141-150.
[27] Regentova E, Zhang L, Zheng J, et al. Microcalcification detection based on wavelet domain hidden Markov tree model: Study for inclusion to computer aided diagnostic prompting system [J]. Medical Physics, 2007, 34(6): 2206-2219.
[28] Rose C, Turi D, Williams A, et al. Web Services for the DDSM and Digital Mammography Research [M]. Digital Mammography. 2006: 376-383.
[29] Sajda P, Spence C, Pearson J. Learning contextual relationships in mammograms using a hierarchical pyramid neural network [J]. IEEE Trans Med Imag, 2002, 21(3): 239-250.
[30] Soltanian-Zadeh H, Rafiee-Rad F, Pourabdollah-Nejad D S. Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms [J]. Pattern Recognition, 2004, 37(10): 1973-1986.
[31] Strickland R N, Hahn H I. Wavelet transforms for detecting microcalcifications in mammograms [J]. IEEE Trans Med Imag, 1996, 15(2): 218-229.
[32] Tao D, Li X, Wu X, et al. Supervised tensor learning [J]. J Knowl Inf Syst, 2007, 13(1): 1-42.
[33] Tao D, Li X, Wu X, et al. General Tensor discriminant analysis and gabor Features for gait recognition [J]. IEEE Trans Pattern Anal Mach Intell, 2007, 29(10): 1700-1715.
[34] Tsujii O, Freedman M T, Mun S K. Classification of microcalcifications in digital mammograms using trend-oriented radial basis function neural network [J]. Pattern Recognition, 1999, 32(5): 891-903.
[35] Verma B, Zakos J. A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques [J]. IEEE Trans Inf Technol Biomed, 2001, 5(1): 46-54.
[36] Wang T, Karayiannis N. Detection of microcalcifications in digital mammograms using wavelets [J]. IEEE Trans Med Imag, 1998, 17(4): 498-509.
[37] Wei L, Yang Y, Nishikawa R M, et al. Relevance vector machine for automatic detection of clustered microcalcifications [J]. IEEE Trans Med Imag, 2005, 24(10): 1278-1285.
[38] Yoshida H, Doi K, Nishikawa R M, et al. An improved computer-assisted diagnostic scheme using wavelet transform for detecting clustered microcalcifications in digital mammograms [J]. Academic Radiology, 1996, 3(8): 621-627.
[39] Yu S-N, Li K-Y, Huang Y-K. Detection of microcalcifications in digitalmammograms using wavelet filter and Markov random field model [J]. Computerized Medical Imaging and Graphics, 2006, 30(3): 163-173.
[40] Zhang L, Sankar R, Qian W. Advances in micro-calcification clusters detection in mammography [J]. Computers in Biology and Medicine, 2002, 32(6): 515-528.
[41] Zhang W, Doi K, Giger M L, et al. Computerized detection of clustered microcalcifications in digital mammograms using a shift-invariant artificial neural network [J]. Medical Physics, 1994, 21(4): 517-524.
[42] Zheng B, Qian W, Clarke L P. Digital mammography: mixed feature neural network with spectral entropy decision for detection of microcalcifications [J]. IEEE Trans Med Imag, 1996, 15(5): 589-597.
[1] Bazaraa M, Sherali H, Shetty C. Nonlinear programming: theory and algorithms [M]. 3rd ed. Malden: Wiley-interscience, 2006.
[2] Bertsekas D, Homer M, Logan D, et al. Nonlinear programming [M]. Nashua, NH: Athena scientific, 1995.
[3] Cai D, He X, Han J, Subspace learning based on tensor analysis(UIUCDCS-R- 2005-2572) [R]: Dept. of Computer Science Tech., Univ. of Illinois at Urbana-Champaign, 2005.
[4] El-Naqa I, Yongyi Y, Wernick M N, et al. A support vector machine approach for detection of microcalcifications [J]. IEEE Trans Med Imag, 2002, 21(12): 1552-1563.
[5] Fu Y, Huang T S. Image classification using correlation tensor analysis [J]. IEEE Trans Image Process, 2008, 17(2): 226-234.
[6] Fu Y, S. H T. Image Classification using correlation tensor analysis [J]. IEEE Trans Image Process, 2008, 17(2): 226-234.
[7] Itskov M. Tensor algebra and tensor analysis for engineers: with applications to continuum mechanics [M]. 2nd ed. Heidelberg: Springer Verlag, 2007.
[8] Jayadeva R, Chandra S. Twin support vector machines for pattern classification [J]. IEEE Trans Pattern Anal Mach Intell, 2007, 905-910.
[9] Lepore N, Brun C, Yi-Yu C, et al. Generalized tensor-based morphometry of HIV/AIDS using multivariatestatistics on deformation tensors [J]. IEEE Trans Med Imag, 2008, 27(1): 129-141.
[10] Li J, Zhang L, Tao D, et al. A Prior Neurophysiologic knowledge free tensor-Based scheme for single trial EEG classification [J]. IEEE Trans Neural Syst Rehabil Eng, 2009, 17(2): 107-115.
[11] Li X, S. L, Yan S, et al. Discriminant locally linear embedding With high-order tensor data [J]. IEEE Trans Syst, Man, Cybern B, Cybern, 2008, 38(2): 342-352.
[12] Lu H, Plataniotis K N, Venetsanopoulos A N. MPCA: Multilinear principal component analysis of tensor objects [J]. IEEE Trans Neural Netw, 2008, 19(1): 18-39.
[13] N. R, S. B. Dimensionality reduction based on tensor modeling for classification methods [J]. IEEE Trans Geosci Remote Sens, 2009, 47(4): 1123-1131.
[14] Pattichis M S, Bovik A C. Analyzing image structure by multidimensional frequency modulation [J]. IEEE Trans Pattern Anal Mach Intell, 2007, 29(5): 753-766.
[15] Rose C, Turi D, Williams A, et al. Web Services for the DDSM and digital mammography research [M]. Digital Mammography. 2006: 376-383.
[16] Sung Won P, Savvides M. Individual Kernel Tensor-Subspaces for Robust Face Recognition: A computationally efficient tensor framework without requiring mode factorization [J]. IEEE Trans Syst, Man, Cybern B, Cybern, 2007, 37(5): 1156-1166.
[17] Tao D, Li X, Wu X, et al. Supervised tensor learning [J]. J Knowl Inf Syst, 2007, 13(1): 1-42.
[18] Tao D, Li X, Wu X, et al. General tensor discriminant analysis and gabor features for gait recognition [J]. IEEE Trans Pattern Anal Mach Intell, 2007, 29(10): 1700-1715.
[19] Vasilescu M A O, Terzopoulos D. Multilinear (Tensor) ICA and dimensionality reduction[C]. 2007: 818-826.
[20] Wang H, Yan S, Huang T, et al. A convergent solution to tensor subspace learning[C]. in Proceedings of the International Joint Conference on Artificial Intelligence(IJCAI-07), 2007: 629-634.
[21] Wei L, Yang Y, Nishikawa R M, et al. A study on several machine-learning methods for classification of malignant and benign clustered microcalcifications [J]. IEEE Trans Med Imag, 2005, 24(3): 371-380.
[22] Wei L, Yang Y, Nishikawa R M, et al. Relevance vector machine for automatic detection of clustered microcalcifications [J]. IEEE Trans Med Imag, 2005, 24(10): 1278-1285.
[23] Wu F, Liu Y, Zhuang Y. Tensor-based transductive learning for multimodality video semantic concept detection [J]. IEEE Trans Multimedia, 2009, 11(5): 868-878.
[24] Xiaofei He, Deng Cai, Niyogi P. Tensor subspace analysis[C]. in Proceedings of the Advances in Neural Information Processing Systems( NIPS2005), 2005: 499-506.
[25] Xu D, Yan S, Tao D, et al. Human gait recognition with matrix representation [J]. IEEE Trans Circuits Syst Video Technol, 2006, 16(7): 896-903.
[26] Xu D, Yan S, Zhang L, et al. Reconstruction and recognition of tensor-based objects with concurrent subspaces analysis [J]. IEEE Trans Circuits Syst Video Technol, 2008, 18(1): 36-47.
[27] Yan S, Xu D, Yang Q, et al. Discriminant analysis with tensor representation [M].IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05). IEEE Computer Society. 2005: 526-532.
[28] Yan S, Xu D, Yang Q, et al. Multilinear discriminant analysis for face recognition [J]. IEEE Trans Image Process, 2007, 16(1): 212-220.
[29] Zhang E, Yeh H, Zhongzang L, et al. Asymmetric tensor analysis for flow visualization [J]. IEEE Trans Vis Comput Graphics, 2009, 15(1): 106-122.
[1] Bocchi L, Coppini G, Nori J, et al. Detection of single and clustered micro calcifications in mammograms using fractals models and neural networks [J]. Medical Engineering & Physics, 2004, 26(4): 303-312.
[2] Boyd J P. Chebyshev and fourier spectral methods [M]. North Chelmsfo: Courier Dover Publications, 2001.
[3] Breiman L. Bagging Predictors [J]. Machine Learning, 1996, 24(2): 123-140.
[4] Cheng H-D, Lui Y M, Freimanis R I. A novel approach to micro calcification detection using fuzzy logic technique [J]. IEEE Trans Med Imag, 1998, 17(3): 442-450.
[5] Cheng H D, Cai X, Chen X, et al. Computer-aided detection and classification of micro calcifications in mammograms: a survey [J]. Pattern Recognition, 2003, 36(12): 2967-2991.
[6] Cheng H D, Wang J, Shi X. Micro calcification detection using fuzzy logic and scale space approaches [J]. Pattern Recognition, 2004, 37(2): 363-375.
[7] Department T D, Dietterich T G. Machine learning research: four current directions [J]. AI Magazine, 1997, 18(1): 97-136.
[8] Duda R O, Hart P E, Stork D G. Pattern classification [M]. 2nd ed. New York: John Wiley & Sons, Inc., 2001.
[9] El-Naqa I, Yongyi Y, Wernick M N, et al. A support vector machine approach for detection of micro calcifications [J]. IEEE Trans Med Imag, 2002, 21(12): 1552-1563.
[10] Elter M, Horsch A. CADx of mammographic masses and clustered microcalcifications: Areview [J]. Medical Physics, 2009, 36(6): 2052-2068.
[11] Fu J C, Lee S K, Wong S T C, et al. Image segmentation feature selection and pattern classification for mammographic microcalcifications [J]. Computerized Medical Imaging and Graphics, 2005, 29(6): 419-429.
[12] Gavrielides M A, Lo J Y, Floyd J C E. Parameter optimization of a computer-aided diagnosis scheme for the segmentation of micro calcificationclusters in mammograms [J]. Medical Physics, 2002, 29(4): 475-483.
[13] Ghazavi S N, Liao T W. Medical data mining by fuzzy modeling with selected features [J]. Artificial Intelligence inMedicine, 2008, 43(3): 195-206.
[14] Halkiotis S, Botsis T, Rangoussi M. Automatic detection of clustered microcalcifications in digital mammograms using mathematical morphology and neural networks [J]. Signal Processing, 2007, 87(7): 1559-1568.
[15] Heng-Da C, Yui Man L, Freimanis R I. A novel approach to micro calcification detection using fuzzy logic technique [J]. IEEE Trans Med Imag, 1998, 17(3): 442-450.
[16] Jesneck J L, Nolte L W, Baker J A, et al. Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis [J]. Medical Physics, 2006, 33(8): 2945-2954.
[17] Kocur C M, Rogers S K, Myers L R, et al. Using neural networks to select wavelet features for breast cancer diagnosis [J]. IEEE Eng Med Biol Mag, 1996, 15(3): 95-102, 108.
[18] Krogh A, Vedelsby J. Neural network ensembles, cross validation, and active learning [J]. Advances in neural information processing systems(NIPS 1995), 1995,231-238.
[19] Ming L, Zhi-Hua Z. Improve Computer-aided diagnosis with machine learning techniques using undiagnosed samples [J]. IEEE Trans Syst, Man, Cybern A, 2007,37(6): 1088-1098.
[20] Pal N R, Bhowmick B, Patel S K, et al. A multi-stage neural network aided system for detection of microcalcifications in digitized mammograms [J]. Neurocomputing, 2008, 71(13-15): 2625-2634.
[21] Papadopoulos A, Fotiadis D I, Likas A. Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines [J]. Artificial Intelligence inMedicine, 2005, 34(2): 141-150.
[22] Retico A, Delogu P, Fantacci M E, et al. A scalable computer-aided detection system for micro calcification cluster identification in a pan-European distributed database of mammograms [J]. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, 2006, 569(2): 601-605.
[23] Schapire R, Singer Y. Improved boosting algorithms using confidence-rated predictions [J]. Machine Learning, 1999, 37(3): 297-336.
[24] Schapire R E. The strength of weak learnability [J]. Machine Learning, 1990, 5(2): 197-227.
[25] Tamura H, Mori S, Yamawaki T. Textural features corresponding to visual perception [J]. IEEE Trans Syst, Man, Cybern, 1978, 8(6): 460-473.
[26] Tsong-Wuu L, Yun-Feng C. A comparative study of Zernike moments [C]. inProceedings of the IEEE/WIC International Conference on Web Intelligence, 2003:516-519.
[27] Veldkamp W J H, Karssemeijer N, Otten J D M, et al. Automated classificationof clustered micro calcifications into malignant and benign types [J]. Medical Physics, 2000, 27(11): 2600-2608.
[28] Verma B, Zhang P. A novel neural-genetic algorithm to find the most significant combination of features in digital mammograms [J]. Applied Soft Computing, 2007, 7(2): 612-625.
[29] Wei L, Yang Y, Nishikawa R M, et al. A study on several machine-learning methods for classification of malignant and benign clustered micro calcifications [J]. IEEE Trans Med Imag, 2005, 24(3): 371-380.
[30] Wei L, Yang Y, Nishikawa R M, et al. Relevance vector machine for automatic detection of clustered micro calcifications [J]. IEEE Trans Med Imag, 2005, 24(10): 1278-1285.
[31] Yu S, Guan L. A CAD system for the automatic detection of clustered micro calcifications in digitized mammogram films [J]. IEEE Trans Med Imag, 2000, 19(2): 115-126.
[1] Agrafiotis D. Stochastic proximity embedding [J]. Journal of computational chemistry, 2003, 24(10): 1215-1221.
[2] Baudat G, Anouar F. Generalized discriminant analysis using a kernel approach [J]. Neural computation, 2000, 12(10): 2385-2404.
[3] Belkin M, Niyogi P. Laplacian eigenmaps for dimensionality reduction and data representation [J]. Neural computation, 2003, 15(6): 1373-1396.
[4] Brand M. Charting a manifold[C]. in Proceedings of the Advances in Neural Information Processing Systems(NIPS 2003), 2003: 985-992.
[5] Cox T F, Cox M A A. Multidimensional scaling [M]. 2nd ed. London: CRC Press, 2000.
[6] De Santo M, Molinara M, Tortorella F, et al. Automatic classification of clustered microcalcifications by a multiple expert system [J]. Pattern Recognition, 2003, 36(7): 1467-1477.
[7] DeMers D, Cottrell G. Non-linear dimensionality reduction[C]. in Proceedings ofthe Advances in neural information processing systems(NIPS 1993), 1993: 580-587.
[8] Donoho D, Grimes C. Hessian eigenmaps: locally linear embedding techniques for high-dimensional data [J]. Proceedings of the National Academy of Sciences, 2003, 100(10): 5591-5596.
[9] Friedman J. Exploratory projection pursuit [J]. Journal of the American statistical association, 1987, 249-266.
[10] Globerson A, Roweis S. Visualizing pairwise similarity via semidefinite embedding[C]. in Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2007: 50-58.
[11] Hall D L, Llinas J. Handbook of multisensor data fusion [M]. London: CRC Press, 2001.
[12] Harman H. Modern factor analysis [M]. Chicago: University of Chicago Press, 1976.
[13] He X, Cai D, Yan S, et al. Neighborhood preserving embedding[C]. in Proceedings of the Tenth IEEE International Conference on Computer Vision(ICCV 2005), 2005: 1208-1213.
[14] He X, Niyogi P. Locality preserving projections[C]. in Proceedings of the Advances in neural information processing systems(NIPS 2003), 2003: 153-160.
[15] Hinton G, Roweis S. Stochastic neighbor embedding[C]. in Proceedings of the Advances in neural information processing systems(NIPS 2003), 2003: 857-864.
[16] Jesneck J L, Nolte L W, Baker J A, et al. Optimized approach to decision fusion of heterogeneous data for breast cancer diagnosis [J]. Medical Physics, 2006, 33(8): 2945-2954.
[17] Jolliffe I T. Principal component analysis [M]. 2nd ed. New York: Springer verlag, 2002.
[18] Lafon S, Lee A. Diffusion maps and coarse-graining: A unified framework for dimensionality reduction, graph partitioning, and data set parameterization [J]. IEEE Trans Pattern Anal Mach Intell, 2006, 28(9): 1393-1403.
[19] Li M, Zhou Z-H. Improve computer-aided diagnosis with machine learning techniques using undiagnosed samples [J]. IEEE Trans Syst, Man, Cybern A, Syst, Humans, 2007, 37(6): 1088-1098.
[20] Maaten L J P, Hinton G E. Visualizing data using t-SNE [J]. Journal of Machine Learning Research, 2008, 9(11): 2431–2456.
[21] Nadler B, Lafon S, Coifman R, et al. Diffusion maps, spectral clustering and reaction coordinates of dynamical systems [J]. Appl Comput Harmon Anal, 2006, 21(1): 113-127.
[22] Psychol J E, Generalis A, Genet S, et al. The use of multiple measurements in taxonomic problems [J]. Ann of Eugenics, 1936, 7(1): 179 - 188.
[23] Roweis S, Saul L. Nonlinear dimensionality reduction by locally linear embedding [J]. Science, 2000, 290(5500): 2323-2326.
[24] Roweis S, Saul L. Global coordination of local linear models[C]. in Proceedings of the Advances in neural information processing systems(NIPS 2002), 2002: 889.
[25] Scholkopf B, Smola A, Muller K. Nonlinear component analysis as a kernel eigenvalue problem [J]. Neural computation, 1998, 10(5): 1299-1319.
[26] Sha F, Saul L. Analysis and extension of spectral methods for nonlinear dimensionality reduction[C]. in Proceedings of the 22nd International Conference on Machine Learning, 2005: 785-792.
[27] Tenenbaum J B. Mapping a manifold of perceptual observations[C]. in Proceedings of the Neural Information Processing Systems(NIPS 1998), 1998: 682-688.
[28] Verbeek J. Learning nonlinear image manifolds by global alignment of local linear models [J]. IEEE Trans Pattern Anal Mach Intell, 2006, 28(8): 1236-1250.
[29] Weinberger K, Saul L. An introduction to nonlinear dimensionality reduction by maximum variance unfolding[C]. in Proceedings of the Twenty-first National Conference on Artificial Intelligence (AAAI-06), 2006: 1683-1686.
[30] Zhang T, Yang J, Zhao D, et al. Linear local tangent space alignment and application to face recognition [J]. Neurocomputing, 2007, 70(7-9): 1547-1553.
[31] Zhang Z, Zha H. Principal manifolds and nonlinear dimension reduction via local tangent space alignment [J]. SIAM J Scientific Computing, 2004, 26(1): 313-338.
[32] Zhou Z, Wu J, Tang W. Ensembling neural networks: many could be better than all [J]. Artificial Intelligence, 2002, 137(1): 239-263.