基于FSVM的医学图像奇异点检测算法研究
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摘要
研制和开发计算机辅助诊断系统是医学图像处理的一个热点。为了解决目前微钙化点检测中普遍存在的假阳性高和效率低的问题,本文提出将改进的SVM算法应用到微钙化点检测问题中,帮助医疗工作者提高诊断的准确率,是非常有意义的。所作的工作主要包括以下几点:
(1)针对经典支持向量机算法存在的问题,为了解决训练速度和训练样本集规模之间的矛盾以及分类速度和支持向量个数之间的矛盾,构造可拒绝的支持向量机,提出了柔性支持向量机FSVM算法。
(2)针对目前的增强算法存在的增强钙化点的同时也增强了正常的背景组织的问题,为便于医生对病灶或感兴趣区域正确地诊断,提出了基于目标特征的非线性灰度重分布图像增强算法。
(3)为了提高微钙化点检测的效率,提出了基于D-S证据理论和FSVM的感兴趣区域检测,通过逐步减少待处理目标,提高检测效率。
(4)为了提高微钙化点检测的真阳性,降低假阳性,提出了基于迭代顺序滤波子空间约束和FSVM的微钙化点检测方法。利用迭代顺序滤波法作为粗检测器,将粗检测误检的非钙化点样本用来训练FSVM,获得具有更高鉴别能力的分类器,有利于提高检测精度,同时可排除大量非钙化点样本,有利于提高检测效率。
Research and development of computer-assisted diagnosis system is a hot topic in the area of medical image processing. On the purpose of solving the problem of high false positive rate and low efficiency in microcalcification detection, an improved support vector machine called flexible SVM proposed here is introduced to help the diagnosis, which is extremely meaningful. The job has been done are mainly presented as follows:(1)Aiming at shortcomings of classical SVM, in order to construct a new algorithm with rejection ability and solve the contradiction between training speed and scale of training set as well as contradiction between classification speed and numbers of support vectors, then a new flexible support vector machine is proposed which will be used as classifier for microcalcifications detection.(2)For many reasons, it is necessary to enhance the medical image and increase the contrast between focuses and background. However, general method not only enhances focuses but also normal tissue. Nonlinear gray re-distribution method for image enhancement based on feature of objects is proposed in this paper.(3)In order to improve efficiency of microcalcification detection, Method for region of interest detection based on D-S evidence theory and flexible support vector machine is proposed. A classifier based on D-S evidence theory information fusion is used as a rough detector, which can eliminate many regions that are not of interests, which made the detection algorithm more efficient.(4)To improve the accuracy and efficiency of microcalcification detection, a method based on iterative rank-order filters subspace restricted and FSVM for microcalcification is proposed. Iterative rank-order filter is used as rough
detector to eliminate large numbers of non-microcalcifications and provide more representative negative samples for training of FS VM. Then FS VM can possess better discrimination ability.
(1)针对经典支持向量机算法存在的问题,为了解决训练速度和训练样本集规模之间的矛盾以及分类速度和支持向量个数之间的矛盾,构造可拒绝的支持向量机,提出了柔性支持向量机FSVM算法。
(2)针对目前的增强算法存在的增强钙化点的同时也增强了正常的背景组织的问题,为便于医生对病灶或感兴趣区域正确地诊断,提出了基于目标特征的非线性灰度重分布图像增强算法。
(3)为了提高微钙化点检测的效率,提出了基于D-S证据理论和FSVM的感兴趣区域检测,通过逐步减少待处理目标,提高检测效率。
(4)为了提高微钙化点检测的真阳性,降低假阳性,提出了基于迭代顺序滤波子空间约束和FSVM的微钙化点检测方法。利用迭代顺序滤波法作为粗检测器,将粗检测误检的非钙化点样本用来训练FSVM,获得具有更高鉴别能力的分类器,有利于提高检测精度,同时可排除大量非钙化点样本,有利于提高检测效率。
Research and development of computer-assisted diagnosis system is a hot topic in the area of medical image processing. On the purpose of solving the problem of high false positive rate and low efficiency in microcalcification detection, an improved support vector machine called flexible SVM proposed here is introduced to help the diagnosis, which is extremely meaningful. The job has been done are mainly presented as follows:(1)Aiming at shortcomings of classical SVM, in order to construct a new algorithm with rejection ability and solve the contradiction between training speed and scale of training set as well as contradiction between classification speed and numbers of support vectors, then a new flexible support vector machine is proposed which will be used as classifier for microcalcifications detection.(2)For many reasons, it is necessary to enhance the medical image and increase the contrast between focuses and background. However, general method not only enhances focuses but also normal tissue. Nonlinear gray re-distribution method for image enhancement based on feature of objects is proposed in this paper.(3)In order to improve efficiency of microcalcification detection, Method for region of interest detection based on D-S evidence theory and flexible support vector machine is proposed. A classifier based on D-S evidence theory information fusion is used as a rough detector, which can eliminate many regions that are not of interests, which made the detection algorithm more efficient.(4)To improve the accuracy and efficiency of microcalcification detection, a method based on iterative rank-order filters subspace restricted and FSVM for microcalcification is proposed. Iterative rank-order filter is used as rough
detector to eliminate large numbers of non-microcalcifications and provide more representative negative samples for training of FS VM. Then FS VM can possess better discrimination ability.
引文
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3 Azra Alizad, Mostafa Fatemi, E.Wold Lester, et al. Performance of Vibro-Acoustography in Detecting Microcalcifications in Excised Human Breast Tissue: A Study of 74 Tissue Samples. IEEE Trans. on Medical Imaging, 2004, 23(3): 307-312
4 F.Diekmann, S.Diekmann, M.Bollow, et al. Evaluation of a Wavelet-Based Computer-Assisted Detection System for Identifying Microcalcifications in Digital Full-Field Mammography. Acta Radiologica, 2004, 45(2): 136-141
5 W.C.W.Chu, W.W.M.Lam, A.L.M.Pang, et al. Computer-Assisted Detection as a Second Reader in Symptomatic Asian Women with Palpable Breast Cancer. Acta Radiologica, 2004, 45(2): 148-153
6 S.Sentelle, C.Sentelle, M.A.Sutton. Multiresolution-Based Segmentation of Calcifications for the Early Detection of Breast Cancer. Real-Time Imaging, 2002, 8(3): 237-252
7 Wei Qian, Fei Mao, Xuejun Sun. An Improved Method of Region Grouping for Micro-calcification Detection in Digital Mammograms. Computerized Medical Imaging and Graphics, 2002, 26(6): 361-368
8 杨冬云,张晓峰,张晔.基于高阶统计特征及形态学的图像奇异点分割和检测.吉林大学学报(信息科学版),2003,21(5):68-72
9 M.G.Mini, V.P.Devassia, Thomas, et al. Multiplexed Wavelet Transform Technique for Detection of Microcalcification in Digitized Mammograms. Journal of Digital Imaging, 2004, 17(4): 285-291
10 Peter Heinlein, Johann Drexl, Wilfried Schneider. Integrated Wavelets for Enhancement of Microcalcifications in Digital Mammography. IEEE Trans. on Medical Imaging, 2003, 22(3): 402-413
11 H.D.Cheng, Jingli Wang, Xiangjun Shi. Microcalcification Detection Using Fuzzy Logic and Scale Space Approaches. Pattern Recognition, 2004, 37(2): 363-375
12 Du-Yih Tsai, Yongbum Lee, Sekiya Masaru, et al. Medical Image Classification Using Genetic-Algorithm Based Fuzzy-Logic Approach. Journal of Electronic Imaging, 2004, 13(4): 780-788
13 Paul Sajda, Clay Spence, John Pearson. Learning Contextual Relationships in Mammograms Using a Hierarchical Pyramid Neural Network. IEEE Trans. on Medical Imaging, 2002, 21(3): 239-250
14 Liyang Wei, Yongyi Yang, M.Nishikawa Robert, et al. Relevance Vector Machine for Automatic Detection of Clustered Microcalcifications. IEEE Trans. on Medical Imaging, 2005, 24(10): 1278-1285
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16 Liyang Wei, Yongyi Yang, M.Nishikawa Robert, et al. A Study on Several Machine-Learning Methods for Classification of Malignant and Benign Clustered Microcalcifications. IEEE Trans. on Medical Imaging, 2005, 24(3): 371-380
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19 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.中国生物医学工程学报,2002,21(6):536-542
20 Campos, Raul Mata, Vidal, et al. Detection of Microcalcifications by Means of Multiscale Methods and Statistical Techniques. Journal of Digital Imaging, 2000, 13(2): 221-225
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23 柳回春,马树元,吴平东,等.手写体数字识别技术的研究.计算机工程,2003,29(4):24-25
24 Lihua Yang, Y.Suen Ching, Tien D. Bui, et al. Discrimination of Similar Handwritten Numerals Based on Invariant Curvature Features. Pattern Recognition, 2005, 38(7): 947-963
25 A.Christopher, Waring, Xiuwen Liu. Face Detection Using Spectral Histograms and SVMs. IEEE Tram. on System, Man, and Cybernetics-Part B: Cybernetics, 2005, 35(3): 467-476
26 Shaoning Pang, Daijin Kirn, Sung Yang Bang. Face Membership Authentication Using SVM Classification Tree Generated by Membership-Based LLE Data Partition. IEEE Tram. on Neural Networks, 2005, 16(2): 436-446
27 Diaz, Irene, Ranilla, et al. Improving Performance of Text Categorization by Combining Filtering and Support Vector Machines. Journal of the American Society for Information Science and Technology, 2004, 55(7): 579-592
28 Ji He, Ah-Hwee Tan, Chew-Lim Tan. On Machine Learning Methods for Chinese Document Categorization. Applied Intelligence, 2003, 18(3): 311-322
29 Hong Liu, Shangteng Huang. Fuzzy Transductive Support Vector Machines for Hypertext Classification. International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, 2004, 12(1): 21-36
30 Penggang Wang, S-Y William. Tone Recognition of Continuous Cantonese Speech Based on Support Vector Machines. Speech Communication, 2005, 45(1): 49-62
31 Vincent Wan, Steve Renals. Verification Using Sequence Discriminant Support Vector Machines. IEEE Trans. on Speech and Audio Processing, 2005, 13(2): 203-210
32 但志平,王以治,黄艳,等.基于LPC倒谱参数和支持向量机技术的说话人识别系统.电声技术,2004,(3):39-41
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34 钱纬,陈卫,白石磊.基于形态学及SVM思想的病变图象识别方法.中国图象图形学报,2003,8(A)(10):1201-1204
35 K.Begg Rezaul, Marimuthu Palaniswami, Brendan Owen. Support Vector Machines for Automated Gait Classification. IEEE Trans. on Biomedical Engineering, 2005, 52(5): 828-838
36 De Santo, Massimo, Molinara, et al. Automatic Classification of Clustered Microcalcifications by a Multiple Expert System. Pattern Recognition, 2003, 36(7): 1467-1477
37 Hongdun Lin, Being-Tau Chung. Statistical-Based Sub-Band Filtering Technique for Digital Mammogram Enhancement. Biomedical Engineering-Applications, Basis and Communications, 2003, 15(4): 150-156
38 L.Bocchi, G.Coppini, J.Nori, et al. Detection of Single and Clustered Microcalcifications in Mammograms Using Fractals Models and Neural Networks. Medical Engineering and Physics, 2004, 26(4): 303-312
39 张学工.关于统计学习理论与支持向量机.自动化学报,2000,26(1):32-42
40 范昕纬.支持向量机算法的研究及其应用.[浙江大学工学博士学位论文].2003:8-19
41 Mitra Pabitra, C.A.Murthy, K.pal Sankar. A Probabilistic Active Support Vector Learning Algorithm. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2004, 26(3): 413-418
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43 Jianxiong Dong, Y.Zak Ching, Suen. Fast SVM Training Algorithm with Decomposition on Very Large Data Sets. IEEE Trans. on Pattern Analysis Machine Intelligence, 2005, 27(4): 603-618
44 张国宣,孔锐,施泽生,等.一种新的基于聚类的SVM迭代算法.仪器仪表学报,2004,25(4):613-614
45 李琰,崔建福,马镭,等.局部影响分析在支持向量机中的应用.云南大学学报(自然科学版),2004,26(3):204-207
46 刘向东,陈兆乾.一种快速支持向量机分类算法的研究.计算机研究与发展,2004,41(8):1327-1332
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55 San-Kan Lee, Chien-Shun Lo, Chuin-Mu Wang. A Computer-Aided Design Mammogramphy Screening System for Detection and Classification of Microcalcifications. International Journal of Medical Informatics, 2000, 60(1): 29-57
56 A.Papadopoulos, D.I.Fotidis, A.Likas. An Automatic Microcalcification Detection System Based on Hybrid Neural Network Classifier. Artificial Intelligence in Medicine, 2002, 25(2): 149-167
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60 Jian Yang, David Zhang, F.Alejandro, et al. Two-Dimensional PCA: A New Approach to Appearance-Based Face Representation and Recognition. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2004, 26(1): 131-137
61 A.F.Ali. Spatial and Spectral Features for Early Detection of Microcalcifications in Marnmograms. Journal of Engineering and Applied Science, 2005, 52(2): 355-372
62 J.C.Fu, S.K.Lee, S.T.C.Wong, et al. Image Segmentation Feature Selection and Pattern Classification for Mammographic Microcalcifications. Computerized Medical Imaging and Graphics, 2005, 29(6): 419-429
2 Xiaopeng Cai, Xiaowei Chen, Liming Hu, et al. Computer-Aided Detection and Classification of Microcalcifications in Mammograms: A Survey. Pattern Recognitiort, 2003, 36(12): 2967-2990
3 Azra Alizad, Mostafa Fatemi, E.Wold Lester, et al. Performance of Vibro-Acoustography in Detecting Microcalcifications in Excised Human Breast Tissue: A Study of 74 Tissue Samples. IEEE Trans. on Medical Imaging, 2004, 23(3): 307-312
4 F.Diekmann, S.Diekmann, M.Bollow, et al. Evaluation of a Wavelet-Based Computer-Assisted Detection System for Identifying Microcalcifications in Digital Full-Field Mammography. Acta Radiologica, 2004, 45(2): 136-141
5 W.C.W.Chu, W.W.M.Lam, A.L.M.Pang, et al. Computer-Assisted Detection as a Second Reader in Symptomatic Asian Women with Palpable Breast Cancer. Acta Radiologica, 2004, 45(2): 148-153
6 S.Sentelle, C.Sentelle, M.A.Sutton. Multiresolution-Based Segmentation of Calcifications for the Early Detection of Breast Cancer. Real-Time Imaging, 2002, 8(3): 237-252
7 Wei Qian, Fei Mao, Xuejun Sun. An Improved Method of Region Grouping for Micro-calcification Detection in Digital Mammograms. Computerized Medical Imaging and Graphics, 2002, 26(6): 361-368
8 杨冬云,张晓峰,张晔.基于高阶统计特征及形态学的图像奇异点分割和检测.吉林大学学报(信息科学版),2003,21(5):68-72
9 M.G.Mini, V.P.Devassia, Thomas, et al. Multiplexed Wavelet Transform Technique for Detection of Microcalcification in Digitized Mammograms. Journal of Digital Imaging, 2004, 17(4): 285-291
10 Peter Heinlein, Johann Drexl, Wilfried Schneider. Integrated Wavelets for Enhancement of Microcalcifications in Digital Mammography. IEEE Trans. on Medical Imaging, 2003, 22(3): 402-413
11 H.D.Cheng, Jingli Wang, Xiangjun Shi. Microcalcification Detection Using Fuzzy Logic and Scale Space Approaches. Pattern Recognition, 2004, 37(2): 363-375
12 Du-Yih Tsai, Yongbum Lee, Sekiya Masaru, et al. Medical Image Classification Using Genetic-Algorithm Based Fuzzy-Logic Approach. Journal of Electronic Imaging, 2004, 13(4): 780-788
13 Paul Sajda, Clay Spence, John Pearson. Learning Contextual Relationships in Mammograms Using a Hierarchical Pyramid Neural Network. IEEE Trans. on Medical Imaging, 2002, 21(3): 239-250
14 Liyang Wei, Yongyi Yang, M.Nishikawa Robert, et al. Relevance Vector Machine for Automatic Detection of Clustered Microcalcifications. IEEE Trans. on Medical Imaging, 2005, 24(10): 1278-1285
15 万柏坤,王瑞平,朱欣,等.SVM算法及其在乳腺X片微钙化点自动检测中的应用.电子学报,2004,32(4):587-590
16 Liyang Wei, Yongyi Yang, M.Nishikawa Robert, et al. A Study on Several Machine-Learning Methods for Classification of Malignant and Benign Clustered Microcalcifications. IEEE Trans. on Medical Imaging, 2005, 24(3): 371-380
17 李丙春,耿国华,孙蕾.基于PSVM的乳腺X照片分类器设计.计算机应用,2004,24(7):43-44
18 A.Papadopoulos, D.I.Fotiadis, A.Likas. Characterization of Clustered Microcalcifications in Digitized Mammograms Using Neural Networks and Support Vector Machines. Artificial Intelligence in Medicine, 2005, 34(2): 141-150
19 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.中国生物医学工程学报,2002,21(6):536-542
20 Campos, Raul Mata, Vidal, et al. Detection of Microcalcifications by Means of Multiscale Methods and Statistical Techniques. Journal of Digital Imaging, 2000, 13(2): 221-225
21 刘向东,朱美琳,陈兆乾,等.支持向量机及其在模式识别中的应用.计算机科学,2003,30(6):113-117
22 宋晓峰,陈德创,俞改军,等.支持向量机中优化算法.计算机科学,2003,30(3):12-20
23 柳回春,马树元,吴平东,等.手写体数字识别技术的研究.计算机工程,2003,29(4):24-25
24 Lihua Yang, Y.Suen Ching, Tien D. Bui, et al. Discrimination of Similar Handwritten Numerals Based on Invariant Curvature Features. Pattern Recognition, 2005, 38(7): 947-963
25 A.Christopher, Waring, Xiuwen Liu. Face Detection Using Spectral Histograms and SVMs. IEEE Tram. on System, Man, and Cybernetics-Part B: Cybernetics, 2005, 35(3): 467-476
26 Shaoning Pang, Daijin Kirn, Sung Yang Bang. Face Membership Authentication Using SVM Classification Tree Generated by Membership-Based LLE Data Partition. IEEE Tram. on Neural Networks, 2005, 16(2): 436-446
27 Diaz, Irene, Ranilla, et al. Improving Performance of Text Categorization by Combining Filtering and Support Vector Machines. Journal of the American Society for Information Science and Technology, 2004, 55(7): 579-592
28 Ji He, Ah-Hwee Tan, Chew-Lim Tan. On Machine Learning Methods for Chinese Document Categorization. Applied Intelligence, 2003, 18(3): 311-322
29 Hong Liu, Shangteng Huang. Fuzzy Transductive Support Vector Machines for Hypertext Classification. International Journal of Uncertainty, Fuzziness and Knowlege-Based Systems, 2004, 12(1): 21-36
30 Penggang Wang, S-Y William. Tone Recognition of Continuous Cantonese Speech Based on Support Vector Machines. Speech Communication, 2005, 45(1): 49-62
31 Vincent Wan, Steve Renals. Verification Using Sequence Discriminant Support Vector Machines. IEEE Trans. on Speech and Audio Processing, 2005, 13(2): 203-210
32 但志平,王以治,黄艳,等.基于LPC倒谱参数和支持向量机技术的说话人识别系统.电声技术,2004,(3):39-41
33 阎威武,邵惠鹤.支持向量机分类器在医疗诊断中的应用研究.计算机仿真,2003,20(2):69-70
34 钱纬,陈卫,白石磊.基于形态学及SVM思想的病变图象识别方法.中国图象图形学报,2003,8(A)(10):1201-1204
35 K.Begg Rezaul, Marimuthu Palaniswami, Brendan Owen. Support Vector Machines for Automated Gait Classification. IEEE Trans. on Biomedical Engineering, 2005, 52(5): 828-838
36 De Santo, Massimo, Molinara, et al. Automatic Classification of Clustered Microcalcifications by a Multiple Expert System. Pattern Recognition, 2003, 36(7): 1467-1477
37 Hongdun Lin, Being-Tau Chung. Statistical-Based Sub-Band Filtering Technique for Digital Mammogram Enhancement. Biomedical Engineering-Applications, Basis and Communications, 2003, 15(4): 150-156
38 L.Bocchi, G.Coppini, J.Nori, et al. Detection of Single and Clustered Microcalcifications in Mammograms Using Fractals Models and Neural Networks. Medical Engineering and Physics, 2004, 26(4): 303-312
39 张学工.关于统计学习理论与支持向量机.自动化学报,2000,26(1):32-42
40 范昕纬.支持向量机算法的研究及其应用.[浙江大学工学博士学位论文].2003:8-19
41 Mitra Pabitra, C.A.Murthy, K.pal Sankar. A Probabilistic Active Support Vector Learning Algorithm. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2004, 26(3): 413-418
42 李青,焦李成,周伟达.基于向量投影的支撑向量预选取.计算机学报,2005,28(2):145-152
43 Jianxiong Dong, Y.Zak Ching, Suen. Fast SVM Training Algorithm with Decomposition on Very Large Data Sets. IEEE Trans. on Pattern Analysis Machine Intelligence, 2005, 27(4): 603-618
44 张国宣,孔锐,施泽生,等.一种新的基于聚类的SVM迭代算法.仪器仪表学报,2004,25(4):613-614
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