人体脑图像分割技术研究
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摘要
医学图像分割是医学图像处理和分析领域的基础性经典难题,其中脑部医学图像分割因其重要的应用价值一直是医学图像分割的研究热点。本论文的主要工作是对可视人脑部图像和MRI脑部图像进行分割技术研究,以便提取出特定的组织和结构。论文的创新点包括:
1.提出了一种基于最大熵和数学形态学的可视人脑部彩色图像分割方法。该方法是将原本只用于灰度图像分割的最大熵方法和数学形态学方法有机结合而形成的一种彩色脑图像分割方法,它不仅能对可视人脑部图像进行有效分割, 而且成功提取出了脑组织和脑骨结构。经对多达576幅可视人脑部图像作实验均获得若用单一算法无法实现的满意效果。
2.为适应MRI图象分割的问题提出了一种新的核聚类算法。该方法针对传统模糊核聚类算法当数据类差别很大时,小数据类被误分或被大数据类吞并的缺陷,通过定义一个新的目标函数,为每一个类分配了一个动态权值,可改善聚类效果。实验结果表明,本文算法对脑白质、脑灰质、脑脊液分割结果在降低误分率性能方面均比经典核聚类算法有5%以上的提高。
3.提出了一种分层Mumford-Shah模型的颅脑图像分割方法。该方法针对Mumford-Shah模型分割多目标时初始化的耦合对分割结果有明显影响的问题,提出应用分层Mumford-Shah模型进行图像分割,并运用了改进的图像分割方程进行计算求解的图像分割方法,经对MRI脑图像脑白质,脑灰质和脑脊液的分割实验,取得了良好的效果。
Medical image segmentation is a hard-tough problem in medical image processing and analysis. Among it, brain medical image segmentation is the research focus for its important values. The primary task of my thesis is studying the Visible Human brain image segmentation and MRI brain image segmentation techniques in order to extract the tissues and structures. The achievements of my thesis include: 1. A Visible Human brain color image segmentation method based on maximum entropy has been proposed. Combing the maximum entropy method and Mathematical Morphology method that were only used for gray image originally, our method extracts brain tissues and brain bones that won't be done if single method is used. The examples of 576 brain images show its effectiveness. 2.A new MRI kernel fuzzy clustering algorithm has been proposed for MRI image segmentation. When the difference of the date size is large. The small class may be misclassified or may be merged into big class in fuzzy kernel clustering algorithm. A new objective function is introduced and an additional weighting factor is assigned to each class. Based on this improvement the algorithm gets better performance. The results indicate it could reduce 5% misclassification rate of the white matter、 gray matter and CSF than original algorithm.3.When Mumford-Shah model is used for multi-object segmentation, the initialization influences the results seriously. A novel image segmentation approach based on hierarchical Mumford-Shah model has been proposed in this paper, which gets the solution by solving the improved image segmentation equations. The example of brain MRI image segmentation indicates its effectiveness.
1.提出了一种基于最大熵和数学形态学的可视人脑部彩色图像分割方法。该方法是将原本只用于灰度图像分割的最大熵方法和数学形态学方法有机结合而形成的一种彩色脑图像分割方法,它不仅能对可视人脑部图像进行有效分割, 而且成功提取出了脑组织和脑骨结构。经对多达576幅可视人脑部图像作实验均获得若用单一算法无法实现的满意效果。
2.为适应MRI图象分割的问题提出了一种新的核聚类算法。该方法针对传统模糊核聚类算法当数据类差别很大时,小数据类被误分或被大数据类吞并的缺陷,通过定义一个新的目标函数,为每一个类分配了一个动态权值,可改善聚类效果。实验结果表明,本文算法对脑白质、脑灰质、脑脊液分割结果在降低误分率性能方面均比经典核聚类算法有5%以上的提高。
3.提出了一种分层Mumford-Shah模型的颅脑图像分割方法。该方法针对Mumford-Shah模型分割多目标时初始化的耦合对分割结果有明显影响的问题,提出应用分层Mumford-Shah模型进行图像分割,并运用了改进的图像分割方程进行计算求解的图像分割方法,经对MRI脑图像脑白质,脑灰质和脑脊液的分割实验,取得了良好的效果。
Medical image segmentation is a hard-tough problem in medical image processing and analysis. Among it, brain medical image segmentation is the research focus for its important values. The primary task of my thesis is studying the Visible Human brain image segmentation and MRI brain image segmentation techniques in order to extract the tissues and structures. The achievements of my thesis include: 1. A Visible Human brain color image segmentation method based on maximum entropy has been proposed. Combing the maximum entropy method and Mathematical Morphology method that were only used for gray image originally, our method extracts brain tissues and brain bones that won't be done if single method is used. The examples of 576 brain images show its effectiveness. 2.A new MRI kernel fuzzy clustering algorithm has been proposed for MRI image segmentation. When the difference of the date size is large. The small class may be misclassified or may be merged into big class in fuzzy kernel clustering algorithm. A new objective function is introduced and an additional weighting factor is assigned to each class. Based on this improvement the algorithm gets better performance. The results indicate it could reduce 5% misclassification rate of the white matter、 gray matter and CSF than original algorithm.3.When Mumford-Shah model is used for multi-object segmentation, the initialization influences the results seriously. A novel image segmentation approach based on hierarchical Mumford-Shah model has been proposed in this paper, which gets the solution by solving the improved image segmentation equations. The example of brain MRI image segmentation indicates its effectiveness.
引文
[1].章毓晋.图象工程(上册)-图象处理利分析.清华大学出版社,1999:179-215.
[2] J. C. Bezdek, L. O. Hall, and L. P. Clarke. Review of MR image segmentation techniques using pattern recognition. Med Phys, 1993, 20: 1033-1048.
[3] L. P. Clarke, R. P. Velthuizen, M. A. Camacho, J. J. Heine, M. Vaidyanathan, et al. MRI segmentation: methods and applications. Mag. Res. Imag. 1995, 13: 343-368.
[4] Z. Liang. Tissue classification and segmentation of MR images. IEEE Eng. Med. Biol. 1993, 81-85.
[5] A P Zijdenbos and B M Dawant. Brain segmentation and white matter lesion detection in MR images. Critical Reviews in Biomedical Engineering. 1994 22: 401-465.
[6] L P Clarke, R P Velthuizen, S Phuphanich, J D Schellenberg, J A Arrington, and M. Silbiger. MRI: stability of three supervised segmentation techniques. Mag. Res. Imag. 1993, 11: 95-106.
[7] L. O. Hall, A. M. Bensaid, L. P. Clarke, R. P. Velthuizen, M. S. Silbiger, and J. C. Bezdek. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE T. Neural Networks, 1992, 3: 672-682.
[8] M. Vaidyanathan, L. P. Clarke, R. P. Velthuizen, S. Phuphanich, A. M. Bensaid, et al. Comparison of supervised MRI segmentation methods for tumor volume determination during therapy. Mag. Res. Imag. 1995, 13: 719-728.
[9] Cemil Kirbas and Francis K. H. Quek. Vessel Extraction Techniques and Algorithms: A Survey, Proceedings of the Third IEEE Symposium on Biolnformatics and BioEngineering. 2003, 238-245
[10] A. Macovski. Medical imaging systems. Prentice-Hall, 1983.
[11] P. Sprawls. Physical principles of medical imaging. Aspen Publishers, 1993.
[12].罗希平,田捷,诸葛婴等.图像分割方法综述,模式识别与人工智能,1999,12(3):300-312.
[13].林瑶,田捷.医学图像分割方法综述,模式识别与人工智能,2002,15(2):192-204.
[14]. Falcao A X, Udupa J K, Samarasekera S, Sharma S. User-Steered Image Segmentation Paradigms: Live wire and Live Lane. Graphic Models and Image Processing, 1998, 60(4): 233-260.
[15]. Laine A, Fan J. Frame. Representations for texture segmentation, IEEE-IP, 1996, 5(5): 771-780.
[16]. Brejl Marek, Sonka Milan. Edge-Base Image Segmentation: Machine Learning From Examples. Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. 1998, 2: 814-819.
[17]. Nekovei Reza, Sun Ying. Back-Propagation Network and its Configuration for Blood Vessel Detection in Angiograms[J]. IEEE Transaction on Neural Network.
[18]. NazifA. M, Levine M, D. Low Level Image's Segmentation: An expect system. IEEE Trans of PAMI, 1984, PAMI-6:555.
[19].田捷,包尚联,周明全.医学影像处理与分析.北京:电子工业出版社,2003.
[20]. R. C. Dubes, A. K. Jain. Algorithms for clustering data. Englewood Cliffs, Prentice Hall, 1988.
[21]. Zadeh. L. A. Fuzzy sets. Information and control, 1965, 8: 338-353.
[22]. E. H. Ruspini. A new approach to clustering. Inf. Cont, 1969, 15: 22-32.
[23]. J. C. Dunn. Well-separated clusters and the optimal fuzzy partitions. J. Cybernet, 1974, 4: 95-104
[24]. J. C. Dunn. A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters. J. Cybenet, 1974, 3: 32-57.
[25]. J. C. Bezdek. A convergence theorem for the fuzzy ISODATA clustering algorithm. IEEE Trans, PA MI, 1980, 1 (2).
[26]. Coleman G. B, Andrews H. C. Image segmentation by clustering. Proc, IEEE, 1979, 5(67): 773-785.
[27]. Schachter, B. J. Davis, LS. Rosenfeld. A. Some Experiments in Image Segmentation by Clustering of Local Values. PR(11), 1979, 1: 19-28.
[28]. R. Hoffmann, A. Jain. Segmentation and classification of range images. IEEE Trans on Patt Anal Machine Intell. 1987, 9(5): 608-620.
[29]. L. O. Hall et al. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans Neural Networks, 1992, 3(5): 672-682.
[30]. Jian Yu, Qiansheng et al. Analysis of the Weighting Exponent in the FCM. IEEE Trans on Systems And Cybernetics, 2004, 34(1): 634-639.
[31]. Girolami M. Mercer Kernel Based Clustering in Feature Space. IEEE Trans on Neural Networks, 2002, 13(3): 780-784.
[32].张莉,周传达,焦李成.核聚类算法.计算机学报,2002,25(6):587-590.
[33].伍忠东,高新波,谢维信.基于核方法的模糊聚类算法.西安电子科技大学学报,2004.31(4):533-537.
[34]. M. Kass, A. Witkin, D. Terzopoulos. Snakes: active contour models, Int'l J. Comp, 1987, 1(4): 321-331.
[35]. N. Paragios, R. Deriche. Geodesic active regions for motion estimation and tracking. The 7th International Conference on Computer Vision, 1999: 674-688.
[36]. A. Yezzi, A. Tsai, A. Willsky. Fully Global Coupled Curve Evolution Equations for Image Segmentation. LIDS Technical Report, January, 1999.
[37]. V. Caselles, ECatte, et al. A geometric model for active contours. Numersche Mathematik, 1993, 66: 1-31.
[38]. R. Malladi, J. A. Sethian. Shape modeling with front propagation:a level set approach. IEEE Trans. Patt. Anal. Mach. Intell, 1995, 17(2): 158-175.
[39]. K. Siddiqi, Y. B. Lauzi, et al. Area and length minimizing flows for shape segmentation. IEEE Trans on Imag Proc, 1998, 7(3): 433-443.
[40]. S. Osher, J. A. Sethian. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Computational Physics, 1988, 79(1): 12-49.
[41]. J. A. Sethian. A review of recent numerical algorithms for hypersurfaces moving with curvature dependent speed. J. Differential Geometry, 1989, 31: 131-161.
[42]. L. Vese, T. F. Chan. A multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model[J]. International Journal of Computer Vision, 2002, 50(3): 271-293.
[43].李俊,杨新,施鹏飞.基于Mumford-Shah模型的快速水平集图像分割方法[J].计算机学报,2002,25(11):1175-1183.
[2] J. C. Bezdek, L. O. Hall, and L. P. Clarke. Review of MR image segmentation techniques using pattern recognition. Med Phys, 1993, 20: 1033-1048.
[3] L. P. Clarke, R. P. Velthuizen, M. A. Camacho, J. J. Heine, M. Vaidyanathan, et al. MRI segmentation: methods and applications. Mag. Res. Imag. 1995, 13: 343-368.
[4] Z. Liang. Tissue classification and segmentation of MR images. IEEE Eng. Med. Biol. 1993, 81-85.
[5] A P Zijdenbos and B M Dawant. Brain segmentation and white matter lesion detection in MR images. Critical Reviews in Biomedical Engineering. 1994 22: 401-465.
[6] L P Clarke, R P Velthuizen, S Phuphanich, J D Schellenberg, J A Arrington, and M. Silbiger. MRI: stability of three supervised segmentation techniques. Mag. Res. Imag. 1993, 11: 95-106.
[7] L. O. Hall, A. M. Bensaid, L. P. Clarke, R. P. Velthuizen, M. S. Silbiger, and J. C. Bezdek. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE T. Neural Networks, 1992, 3: 672-682.
[8] M. Vaidyanathan, L. P. Clarke, R. P. Velthuizen, S. Phuphanich, A. M. Bensaid, et al. Comparison of supervised MRI segmentation methods for tumor volume determination during therapy. Mag. Res. Imag. 1995, 13: 719-728.
[9] Cemil Kirbas and Francis K. H. Quek. Vessel Extraction Techniques and Algorithms: A Survey, Proceedings of the Third IEEE Symposium on Biolnformatics and BioEngineering. 2003, 238-245
[10] A. Macovski. Medical imaging systems. Prentice-Hall, 1983.
[11] P. Sprawls. Physical principles of medical imaging. Aspen Publishers, 1993.
[12].罗希平,田捷,诸葛婴等.图像分割方法综述,模式识别与人工智能,1999,12(3):300-312.
[13].林瑶,田捷.医学图像分割方法综述,模式识别与人工智能,2002,15(2):192-204.
[14]. Falcao A X, Udupa J K, Samarasekera S, Sharma S. User-Steered Image Segmentation Paradigms: Live wire and Live Lane. Graphic Models and Image Processing, 1998, 60(4): 233-260.
[15]. Laine A, Fan J. Frame. Representations for texture segmentation, IEEE-IP, 1996, 5(5): 771-780.
[16]. Brejl Marek, Sonka Milan. Edge-Base Image Segmentation: Machine Learning From Examples. Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. 1998, 2: 814-819.
[17]. Nekovei Reza, Sun Ying. Back-Propagation Network and its Configuration for Blood Vessel Detection in Angiograms[J]. IEEE Transaction on Neural Network.
[18]. NazifA. M, Levine M, D. Low Level Image's Segmentation: An expect system. IEEE Trans of PAMI, 1984, PAMI-6:555.
[19].田捷,包尚联,周明全.医学影像处理与分析.北京:电子工业出版社,2003.
[20]. R. C. Dubes, A. K. Jain. Algorithms for clustering data. Englewood Cliffs, Prentice Hall, 1988.
[21]. Zadeh. L. A. Fuzzy sets. Information and control, 1965, 8: 338-353.
[22]. E. H. Ruspini. A new approach to clustering. Inf. Cont, 1969, 15: 22-32.
[23]. J. C. Dunn. Well-separated clusters and the optimal fuzzy partitions. J. Cybernet, 1974, 4: 95-104
[24]. J. C. Dunn. A fuzzy relative of the ISODATA process and its use in detecting compact well separated clusters. J. Cybenet, 1974, 3: 32-57.
[25]. J. C. Bezdek. A convergence theorem for the fuzzy ISODATA clustering algorithm. IEEE Trans, PA MI, 1980, 1 (2).
[26]. Coleman G. B, Andrews H. C. Image segmentation by clustering. Proc, IEEE, 1979, 5(67): 773-785.
[27]. Schachter, B. J. Davis, LS. Rosenfeld. A. Some Experiments in Image Segmentation by Clustering of Local Values. PR(11), 1979, 1: 19-28.
[28]. R. Hoffmann, A. Jain. Segmentation and classification of range images. IEEE Trans on Patt Anal Machine Intell. 1987, 9(5): 608-620.
[29]. L. O. Hall et al. A comparison of neural network and fuzzy clustering techniques in segmenting magnetic resonance images of the brain. IEEE Trans Neural Networks, 1992, 3(5): 672-682.
[30]. Jian Yu, Qiansheng et al. Analysis of the Weighting Exponent in the FCM. IEEE Trans on Systems And Cybernetics, 2004, 34(1): 634-639.
[31]. Girolami M. Mercer Kernel Based Clustering in Feature Space. IEEE Trans on Neural Networks, 2002, 13(3): 780-784.
[32].张莉,周传达,焦李成.核聚类算法.计算机学报,2002,25(6):587-590.
[33].伍忠东,高新波,谢维信.基于核方法的模糊聚类算法.西安电子科技大学学报,2004.31(4):533-537.
[34]. M. Kass, A. Witkin, D. Terzopoulos. Snakes: active contour models, Int'l J. Comp, 1987, 1(4): 321-331.
[35]. N. Paragios, R. Deriche. Geodesic active regions for motion estimation and tracking. The 7th International Conference on Computer Vision, 1999: 674-688.
[36]. A. Yezzi, A. Tsai, A. Willsky. Fully Global Coupled Curve Evolution Equations for Image Segmentation. LIDS Technical Report, January, 1999.
[37]. V. Caselles, ECatte, et al. A geometric model for active contours. Numersche Mathematik, 1993, 66: 1-31.
[38]. R. Malladi, J. A. Sethian. Shape modeling with front propagation:a level set approach. IEEE Trans. Patt. Anal. Mach. Intell, 1995, 17(2): 158-175.
[39]. K. Siddiqi, Y. B. Lauzi, et al. Area and length minimizing flows for shape segmentation. IEEE Trans on Imag Proc, 1998, 7(3): 433-443.
[40]. S. Osher, J. A. Sethian. Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations. J. Computational Physics, 1988, 79(1): 12-49.
[41]. J. A. Sethian. A review of recent numerical algorithms for hypersurfaces moving with curvature dependent speed. J. Differential Geometry, 1989, 31: 131-161.
[42]. L. Vese, T. F. Chan. A multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model[J]. International Journal of Computer Vision, 2002, 50(3): 271-293.
[43].李俊,杨新,施鹏飞.基于Mumford-Shah模型的快速水平集图像分割方法[J].计算机学报,2002,25(11):1175-1183.
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