医学图像分割与重建网格模型简化的研究
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摘要
医学影像处理与分析是目前的一个研究热点问题,是一个多学科交叉的研究领域,是计算机图形学和图像处理在生物医学工程中的重要应用。它涉及数字图像处理、计算机图形学以及医学领域的相关知识。医学影像处理与分析的主要研究内容包括医学图像的预处理;组织或器官的分割与提取;复杂表面多相组织成份三维几何模型的构建;重建模型的表面网格简化;模型的剖切与手术开窗操作等。
本文研究的主要内容为医学图像的预处理、组织或器官的分割和三维重建模型的网格简化。分析和总结了国内外学者在这三个方面的研究,本文研究和探讨了几种新的方法,改善现有常用方法存在的不足。
本文的创新点和独立工作主要体现在以下几个方面:
1.研究和总结了医学影像处理与分析中的医学图像的预处理技术、医学图像的分割技术和虚拟可视化中的三维模型简化技术的现状和存在的问题;
2.改进了各向异性扩散的滤波方法,解决了传统滤波方法在去除噪声的同时,也去除了高频边缘信息的问题,同时改进了各向异性扩散滤波滤波速度慢的不足。实验结果证明了该方法的优点。
3.研究了基于决策树的头颅MRI图像的分割方法。该方法结合了模式识别、传统图像分割技术和头颅组织空间分布的规律,能自适应的分割颅内组织,实验结果表明该方法是可行而且有效的。
4.研究了医学图像序列交互式的分割方法。该方法采用分数阶梯度来改进传统的live wire交互式分割方法,然后运用轮廓插值技术对图像序列进行自动快速的分割。实验结果充分体现了该方法的优点。
5.改进了经典的顶点抽取模型简化方法,重新定义了顶点重要性的度量,提高模型简化的质量,结合自适应八叉树的优点,实现三维模型快速简化。实验结果证实了该方法的优点。
Medical image processing and analysis is the one of popular research projects, which is the project including much different knowledge and is the application of computer graphics and image process in the biomedical engineering. It covers many subjects such as computer graphics, image process and medical knowledge, and it is mainly constituted by the pre-processing of medical image, the segmentation of the apparatus and the tissue, visualization of 3D medical models, simplification of complex models, dividing cubes of models and so on.
The primly contents of this thesis are pre-processing of medical image, image segmentation and the simplification of complex models. After studying and summing up many dissertations which obtained from the domestic and overseas scholars, we propose several new arithmetic, to improve the shortages of the classical’s.
The key work and innovations of this thesis mainly include:
1. Research of the actuality of the pre-processing of medical image, image process and the simplification of complex models.
2. Proposed a improvement of anisotropy filter in pre-processing of medical image, which not only wipes off the noise, but also keeps the high-frequency edge information, and improves the time-consuming shortage. Experiments show its advantage.
3. Presented a new approach of skull’s MRI segmentation based on decision making tree. Combining the pattern recognize and the distributing rule of tissue in the skull, it can auto-adapting segment the objects. Experiments show it is feasible and effective.
4. Presented a improvement approach in medical image mutual segmentation. It improves the live wire arithmetic through using Fractional Differential to replace the integral grads, then segments the medical image series automatically by contour interpolation. Experiments show its advantages.
5. Proposed a improvement of vertex deletion in medical model simplification. It defines a new measurement of vertex essentiality, to advance the quality of simplified models; and uses the advantage of auto-adapt octree to quicken up the speed of the model simplification. Experiments show its advantages.
本文研究的主要内容为医学图像的预处理、组织或器官的分割和三维重建模型的网格简化。分析和总结了国内外学者在这三个方面的研究,本文研究和探讨了几种新的方法,改善现有常用方法存在的不足。
本文的创新点和独立工作主要体现在以下几个方面:
1.研究和总结了医学影像处理与分析中的医学图像的预处理技术、医学图像的分割技术和虚拟可视化中的三维模型简化技术的现状和存在的问题;
2.改进了各向异性扩散的滤波方法,解决了传统滤波方法在去除噪声的同时,也去除了高频边缘信息的问题,同时改进了各向异性扩散滤波滤波速度慢的不足。实验结果证明了该方法的优点。
3.研究了基于决策树的头颅MRI图像的分割方法。该方法结合了模式识别、传统图像分割技术和头颅组织空间分布的规律,能自适应的分割颅内组织,实验结果表明该方法是可行而且有效的。
4.研究了医学图像序列交互式的分割方法。该方法采用分数阶梯度来改进传统的live wire交互式分割方法,然后运用轮廓插值技术对图像序列进行自动快速的分割。实验结果充分体现了该方法的优点。
5.改进了经典的顶点抽取模型简化方法,重新定义了顶点重要性的度量,提高模型简化的质量,结合自适应八叉树的优点,实现三维模型快速简化。实验结果证实了该方法的优点。
Medical image processing and analysis is the one of popular research projects, which is the project including much different knowledge and is the application of computer graphics and image process in the biomedical engineering. It covers many subjects such as computer graphics, image process and medical knowledge, and it is mainly constituted by the pre-processing of medical image, the segmentation of the apparatus and the tissue, visualization of 3D medical models, simplification of complex models, dividing cubes of models and so on.
The primly contents of this thesis are pre-processing of medical image, image segmentation and the simplification of complex models. After studying and summing up many dissertations which obtained from the domestic and overseas scholars, we propose several new arithmetic, to improve the shortages of the classical’s.
The key work and innovations of this thesis mainly include:
1. Research of the actuality of the pre-processing of medical image, image process and the simplification of complex models.
2. Proposed a improvement of anisotropy filter in pre-processing of medical image, which not only wipes off the noise, but also keeps the high-frequency edge information, and improves the time-consuming shortage. Experiments show its advantage.
3. Presented a new approach of skull’s MRI segmentation based on decision making tree. Combining the pattern recognize and the distributing rule of tissue in the skull, it can auto-adapting segment the objects. Experiments show it is feasible and effective.
4. Presented a improvement approach in medical image mutual segmentation. It improves the live wire arithmetic through using Fractional Differential to replace the integral grads, then segments the medical image series automatically by contour interpolation. Experiments show its advantages.
5. Proposed a improvement of vertex deletion in medical model simplification. It defines a new measurement of vertex essentiality, to advance the quality of simplified models; and uses the advantage of auto-adapt octree to quicken up the speed of the model simplification. Experiments show its advantages.
引文
[1] Mallat S, G. A theory for multi resolution signal decomposition[J]. IEEE Trans on PAMI,1989,11(7):674-693.
[2] Mallat S,Hwnag W L. Sigularity detection and Processing with wavelets[J].IEEE Trans on Information Theory, 1992, 38(2):617-643.
[3]章毓晋.图像分割[M].北京:科学出版社, 2001.
[4]夏良正.数字图像处理.南京:东南大学出版社, 1999.
[5]朱秀昌,刘峰,胡栋.数字图像处理与图像通信.北京:邮电大学出版社, 2000
[6] N.R. Pal, K. Pal. A review on image segmentation techniques[J]. Pattern Recognition, 1993, 26(9): 1277-1294
[7] Dzung L. Pham, Chenyang Xu, Jerry L. Prince. A survey of current methods in medical image segmentation[J]. Technical Report JHU/ECE 99-01, John HopkinsUniv, 1998
[8]罗希平,田捷,诸葛婴等.图像分割方法综述[J].模式识别与人工智能, 1999, 12(3): 300-312
[9] Tina Kapur. Model based three dimensional medical image segmentation [J]. Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 1999
[10] Zhu S C,Yuille A. Region competition: Unifying snakes, region growing, and Bayes/MDL for mu1tiband images segmentation. IEEE Trans on PMAI, 1996, 18(9):884-900.
[11] Chen C M,Lu H H S, Chen Y L. A discrete region competition approach incorporating weak edge enhancement for ultrasound image segmentation. Pattern Recognition Letters, 2003(24): 693-704.
[12] Brathwaite P A, Chandran K B, MePherson D D, et al. Lumen detection in human IVUS images using region-growing. Computers in Cardiology, 1996, (9):37-40.
[13] Yoshida H, Keserei B, Casalino D D, et al. Segmentation of liver tumors in ultrasound Images based on scale-space analysis of the continuous wavelet transform. IEEE international ultrasonics symposium, 1998:1713-1716.
[14] Klingensmith J D, Shekhar R, Vince D G. Evaluation of three-dimensional segmentation algorithms for the identification of luminal and medial-adventitial borders in intravascular ultrasound images. IEEE Trans on Medical Imaging, 2000, 19(10):996-1011.
[15] Yezzi A J, Kumar A. A geometric snake model for segmentation of medical imagery. IEEE Trans on Medical Imaging, 1997, 16(2):199-209.
[16] Xu C, Pham D L, Prinee J L. Medical image segmentation using deformable models. Handbook of Medical Imaging: Medical Image Processing and Analysis, SPIE Press, 2000(2): 129-174.
[17] Xu C Y, Prinee J L. Snakes, shapes and gradient vector flow. IEEE Trans on Image Processing, 1998, 7(3):359-369.
[18] Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. IJCV, 1988, 1(4): 321-331.
[19] Chiu B C Y, Freeman G H, Salama M M A. A segmentation algorithm using dyadic wavelet transform and discrete dynamic contour. Proceedings of CCECE2003, Montreal, 2003(3): 1481-1484.
[20] Constantine K, Ioannis P. Segmentation of ultrasonic images using Support Vector Machines. Pattern Recognition Letters, 2003(24): 715-727.
[21]陈冠饶,王景熙,郑昌琼等.一种基于生物仿真结构的神经网络在超声图像分割中的应用.四川大学学报, 2001, 33(3): 113-116.
[22]蔡勇,陈念年,李波等.超声医学图像的自动分割.兰州交通大学学报, 2004, 23(4): 61-64.
[23] Loupas T. An Adaptive Weighted Median Filter for Speckle Suppression in Medical Ultrasonic Image [J]. IEEE Trans on Circuits System,1989, CAS-36(l): 129-135.
[24] Lindegerg T. Scale-space theory in computer vision. London, 1994
[25] A Hummel. Representations based on zero-crossing in scale-space[C]. in Proc. IEEE Computer Vision and Pattern Recognition conf. 1986. 6: 204-209
[26] Perona, maalik. Scale and Edge Detection[J]. IEEE Trans Pattern Analysis and Machine Intelligence, 1990:PAMI-194
[27] Weikert J. Multi scale texture enhancement in Lecture Notes in Computer Science [M]. Berlin, 1995, 970:230
[28] Goodman J. W. Some fundamental properties of speckle[J]. Opt. Soc. Amer, 1976, 66(11):1145-1150
[29] Abbortt J, G, Thurstone F. L. Acoustic Speckle Theory and experimental analysis[J]. Ultrasound Image, 1979, l:303-324
[30]边肇祺,张学工等,模式识别.北京:清华出版社. 2000.
[31] Lee SU,Chung S Y. A comparative performance study of several global thresholding techniques for segmentation. Computer Vision, Graphics and Image Processing, 1990, 52: 171-190.
[32]侯格贤,吴成柯一种结合GA的自适应目标分割方法.西安电子科技大学学报, 1998, 25(2):227
[33] Oldham K B, Spanier J. The fractional calculus[M]. New York and London; Academic Press, 1974.
[34] A. X. Falcao, J. K. Udapa, S. Samarasekera and B. E. Hirsch. User-steered image boundary segmentation. In Proceeding of SPIE on Medical Imaging, Newport Beach, CA,1996, 2710:278~288
[35]蒲亦菲.将分数阶微分演算引入数字图像处理[J].四川大学学报:工程科学版, 2007, 39(3)
[36] Canny JF. A Computational Approach to Edge Detection [J]. IEEE Trans. On Pattern Analysis and Machine Intelligent,1986,8(6): 6792
[37] Jonathan Cohen, Amitabh Varshney, etc. Simplification envelopes. In SIGGRAPH’96, www.cs.unc.edu/~geom/envelope.html
[38] Helman J L, Hesselink L. 1991. Visualizing Vector Filed Topology in Fluid Flows. IEEE Computer Graphics & Applications, May, 36~46
[39] Schroeder W J, Zarge J A, Lorensen W E. 1992. Decimation of Triangle Meshes. Computer Graphics, Proceedings, Aug. , 65~70
[40] Turk G. 1992. Re-Tiling Polygonal Surfaces. Computer Graphics, Processing, Aug. , 55~64
[41] Cohen-or D, Fleishman S, 1995. An Incremental Alignment Algorithm for Parallel Volume Rendering. Eurograhpics’95 Proceedings, 123~133
[42] Kalvin A D, Taylor R H. 1996. Superfaces: Polygonal Mesh Simplification with Bounded Error. IEEE Computer Graphics and Application, May, 64~77
[43] Hoppe H. 1996. Progressive Meshes. Computer Graphics, Proceeding , Aug., 99~108
[44] Gueziec A. 1996. Surface Simplification inside a Tolerance Volume. IBM Watson Research Center Technical Report, RC 20440
[45] Hoppe H. 1997. View-Dependent Refinement of Progressive Meshes. Computer Graphics, Proceedings, Aug. 189~198
[46] Hugues Hoppe. Tony DeRose, Tom Duchamp, John McDonald, and Werner Stuetzle. Mesh optimization. InSIGGRAPH’93 Conference Proceedings, 1993, 19~26
[47] Kok-Lim Low, Tiow-Seng Tan. Model simplification using vertex-clustering. In 1997 Symposium on Interactive 3D Graphics, ACM SIGGRAAPH
[48] Kalvin A D, Taylor R H. Surperfaces: polygonal mesh simplification with bounded error. IEEE Computer Graphics and Application, 1996, 16(3):64~77
[49] Michael Lounsbery, Tony DeRose. Multiresolution analysis for surfaces of arbitrary topological type [Report]. Washington: University of Washington, January 1994, ftp.cs.washington.edu/pub/graphics/LounsPhd.ps.Z
[50] Greg Turk. Retiling polygonal surfaces. In SIGGRAPH’92 Conference Proceedings, 1992, 55~64
[51] W. Schroder, J. Zarge, and W. Lorensen. Decimation of triangle meshes. Computer Graphics, 1992, 26(2):65~70
[2] Mallat S,Hwnag W L. Sigularity detection and Processing with wavelets[J].IEEE Trans on Information Theory, 1992, 38(2):617-643.
[3]章毓晋.图像分割[M].北京:科学出版社, 2001.
[4]夏良正.数字图像处理.南京:东南大学出版社, 1999.
[5]朱秀昌,刘峰,胡栋.数字图像处理与图像通信.北京:邮电大学出版社, 2000
[6] N.R. Pal, K. Pal. A review on image segmentation techniques[J]. Pattern Recognition, 1993, 26(9): 1277-1294
[7] Dzung L. Pham, Chenyang Xu, Jerry L. Prince. A survey of current methods in medical image segmentation[J]. Technical Report JHU/ECE 99-01, John HopkinsUniv, 1998
[8]罗希平,田捷,诸葛婴等.图像分割方法综述[J].模式识别与人工智能, 1999, 12(3): 300-312
[9] Tina Kapur. Model based three dimensional medical image segmentation [J]. Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 1999
[10] Zhu S C,Yuille A. Region competition: Unifying snakes, region growing, and Bayes/MDL for mu1tiband images segmentation. IEEE Trans on PMAI, 1996, 18(9):884-900.
[11] Chen C M,Lu H H S, Chen Y L. A discrete region competition approach incorporating weak edge enhancement for ultrasound image segmentation. Pattern Recognition Letters, 2003(24): 693-704.
[12] Brathwaite P A, Chandran K B, MePherson D D, et al. Lumen detection in human IVUS images using region-growing. Computers in Cardiology, 1996, (9):37-40.
[13] Yoshida H, Keserei B, Casalino D D, et al. Segmentation of liver tumors in ultrasound Images based on scale-space analysis of the continuous wavelet transform. IEEE international ultrasonics symposium, 1998:1713-1716.
[14] Klingensmith J D, Shekhar R, Vince D G. Evaluation of three-dimensional segmentation algorithms for the identification of luminal and medial-adventitial borders in intravascular ultrasound images. IEEE Trans on Medical Imaging, 2000, 19(10):996-1011.
[15] Yezzi A J, Kumar A. A geometric snake model for segmentation of medical imagery. IEEE Trans on Medical Imaging, 1997, 16(2):199-209.
[16] Xu C, Pham D L, Prinee J L. Medical image segmentation using deformable models. Handbook of Medical Imaging: Medical Image Processing and Analysis, SPIE Press, 2000(2): 129-174.
[17] Xu C Y, Prinee J L. Snakes, shapes and gradient vector flow. IEEE Trans on Image Processing, 1998, 7(3):359-369.
[18] Kass M, Witkin A, Terzopoulos D. Snakes: active contour models. IJCV, 1988, 1(4): 321-331.
[19] Chiu B C Y, Freeman G H, Salama M M A. A segmentation algorithm using dyadic wavelet transform and discrete dynamic contour. Proceedings of CCECE2003, Montreal, 2003(3): 1481-1484.
[20] Constantine K, Ioannis P. Segmentation of ultrasonic images using Support Vector Machines. Pattern Recognition Letters, 2003(24): 715-727.
[21]陈冠饶,王景熙,郑昌琼等.一种基于生物仿真结构的神经网络在超声图像分割中的应用.四川大学学报, 2001, 33(3): 113-116.
[22]蔡勇,陈念年,李波等.超声医学图像的自动分割.兰州交通大学学报, 2004, 23(4): 61-64.
[23] Loupas T. An Adaptive Weighted Median Filter for Speckle Suppression in Medical Ultrasonic Image [J]. IEEE Trans on Circuits System,1989, CAS-36(l): 129-135.
[24] Lindegerg T. Scale-space theory in computer vision. London, 1994
[25] A Hummel. Representations based on zero-crossing in scale-space[C]. in Proc. IEEE Computer Vision and Pattern Recognition conf. 1986. 6: 204-209
[26] Perona, maalik. Scale and Edge Detection[J]. IEEE Trans Pattern Analysis and Machine Intelligence, 1990:PAMI-194
[27] Weikert J. Multi scale texture enhancement in Lecture Notes in Computer Science [M]. Berlin, 1995, 970:230
[28] Goodman J. W. Some fundamental properties of speckle[J]. Opt. Soc. Amer, 1976, 66(11):1145-1150
[29] Abbortt J, G, Thurstone F. L. Acoustic Speckle Theory and experimental analysis[J]. Ultrasound Image, 1979, l:303-324
[30]边肇祺,张学工等,模式识别.北京:清华出版社. 2000.
[31] Lee SU,Chung S Y. A comparative performance study of several global thresholding techniques for segmentation. Computer Vision, Graphics and Image Processing, 1990, 52: 171-190.
[32]侯格贤,吴成柯一种结合GA的自适应目标分割方法.西安电子科技大学学报, 1998, 25(2):227
[33] Oldham K B, Spanier J. The fractional calculus[M]. New York and London; Academic Press, 1974.
[34] A. X. Falcao, J. K. Udapa, S. Samarasekera and B. E. Hirsch. User-steered image boundary segmentation. In Proceeding of SPIE on Medical Imaging, Newport Beach, CA,1996, 2710:278~288
[35]蒲亦菲.将分数阶微分演算引入数字图像处理[J].四川大学学报:工程科学版, 2007, 39(3)
[36] Canny JF. A Computational Approach to Edge Detection [J]. IEEE Trans. On Pattern Analysis and Machine Intelligent,1986,8(6): 6792
[37] Jonathan Cohen, Amitabh Varshney, etc. Simplification envelopes. In SIGGRAPH’96, www.cs.unc.edu/~geom/envelope.html
[38] Helman J L, Hesselink L. 1991. Visualizing Vector Filed Topology in Fluid Flows. IEEE Computer Graphics & Applications, May, 36~46
[39] Schroeder W J, Zarge J A, Lorensen W E. 1992. Decimation of Triangle Meshes. Computer Graphics, Proceedings, Aug. , 65~70
[40] Turk G. 1992. Re-Tiling Polygonal Surfaces. Computer Graphics, Processing, Aug. , 55~64
[41] Cohen-or D, Fleishman S, 1995. An Incremental Alignment Algorithm for Parallel Volume Rendering. Eurograhpics’95 Proceedings, 123~133
[42] Kalvin A D, Taylor R H. 1996. Superfaces: Polygonal Mesh Simplification with Bounded Error. IEEE Computer Graphics and Application, May, 64~77
[43] Hoppe H. 1996. Progressive Meshes. Computer Graphics, Proceeding , Aug., 99~108
[44] Gueziec A. 1996. Surface Simplification inside a Tolerance Volume. IBM Watson Research Center Technical Report, RC 20440
[45] Hoppe H. 1997. View-Dependent Refinement of Progressive Meshes. Computer Graphics, Proceedings, Aug. 189~198
[46] Hugues Hoppe. Tony DeRose, Tom Duchamp, John McDonald, and Werner Stuetzle. Mesh optimization. InSIGGRAPH’93 Conference Proceedings, 1993, 19~26
[47] Kok-Lim Low, Tiow-Seng Tan. Model simplification using vertex-clustering. In 1997 Symposium on Interactive 3D Graphics, ACM SIGGRAAPH
[48] Kalvin A D, Taylor R H. Surperfaces: polygonal mesh simplification with bounded error. IEEE Computer Graphics and Application, 1996, 16(3):64~77
[49] Michael Lounsbery, Tony DeRose. Multiresolution analysis for surfaces of arbitrary topological type [Report]. Washington: University of Washington, January 1994, ftp.cs.washington.edu/pub/graphics/LounsPhd.ps.Z
[50] Greg Turk. Retiling polygonal surfaces. In SIGGRAPH’92 Conference Proceedings, 1992, 55~64
[51] W. Schroder, J. Zarge, and W. Lorensen. Decimation of triangle meshes. Computer Graphics, 1992, 26(2):65~70