规则数据场的三维医学体数据可视化研究
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摘要
本论文针对医学体数据,从科学可视化的角度出发,通过分析大量文献,重点研究了水平集的分割算法和基于分割的三维重建方法。论文所做的主要工作及创新成果包括:
1.讨论了规则数据场、医学体数据可视化分割和三维重建技术的相关概念,分析了当前国内外的发展情况。
2.对水平集分割进行研究,提出了水平集的快速行进法和窄带法相结合的图像分割算法,从规则的医学图像中准确地提取出目标组织器官,为接下来的三维重建做基础;与此同时,根据感兴趣区的特性和其所处的环境合理选取种子点,解决分割中对种子选取的瓶颈问题。
3.根据规则的医学体数据,利用本文提出的分割方法,研究了基于分割的改进的Marching Cubes方法和基于颜色填充的光线投影体绘制方法,用实验表明,比传统的重建方法具有较高的可视化效果。
4.仔细研究了国内外正在普遍使用的VTK(Visualization Toolkit)和ITK(insightsegmentation and registration Toolkit)开发工具包,并用VC++2005实现了我们提出的分割算法和三维重建方法,给出关键代码。
5.用规则医学体数据,在Windows平台上,结合ITK、VTK研究出一套简单的医学可视化系统,该系统实现了三维显示、二维分割、虚拟切片显示、人机交互等功能,具有一定的后续研发参考价值。
This paper mainly studies the segmentation algorithm of Level Set and 3D reconstruction method for medical volume data through the analysis of lots of documents, which from the perspective of scientific visualization. The main work and the innovative achievements include:
1. Discussing the related concepts of the regular data sets, the visualization of medical data segmentation and 3D reconstruction technology, analysis of the current developments at home and abroad.
2. Considering the Level Set segmentation, a image segmentation algorithm combined the Fast-marching with the Narrow-band is developed, extract the organs accurately from the regular medical images. As a foundation of the next 3D reconstruction; Meanwhile, according to the district interested in the character and the environment in which its reasonable selection of seeds, which can solve the bottlenecks of seed selection.
3. According to the regular medical data, use the segmentation method proposed in this paper, a improved Marching Cubes methods based on segmentation and Ray Casting Volume Rending methods based on the color-filled are proposed. The experiments show that it can be better than the traditional method of reconstruction.
4. Research the VTK (Visualization Toolkit) and ITK (insight segmentation and registration Toolkit) which widely used at home and abroad, then achieve our segmentation algorithm and the 3D reconstruction methods with VC + +2005, given the key code.
5. Using the regular medical volume data and the Windows platform, a simple medical visualization system is developed. The system achieved the functions of 3D visualization, 2D segmentation, virtual slices human-computer interaction and etc. It has some value of Follow-up research.
1.讨论了规则数据场、医学体数据可视化分割和三维重建技术的相关概念,分析了当前国内外的发展情况。
2.对水平集分割进行研究,提出了水平集的快速行进法和窄带法相结合的图像分割算法,从规则的医学图像中准确地提取出目标组织器官,为接下来的三维重建做基础;与此同时,根据感兴趣区的特性和其所处的环境合理选取种子点,解决分割中对种子选取的瓶颈问题。
3.根据规则的医学体数据,利用本文提出的分割方法,研究了基于分割的改进的Marching Cubes方法和基于颜色填充的光线投影体绘制方法,用实验表明,比传统的重建方法具有较高的可视化效果。
4.仔细研究了国内外正在普遍使用的VTK(Visualization Toolkit)和ITK(insightsegmentation and registration Toolkit)开发工具包,并用VC++2005实现了我们提出的分割算法和三维重建方法,给出关键代码。
5.用规则医学体数据,在Windows平台上,结合ITK、VTK研究出一套简单的医学可视化系统,该系统实现了三维显示、二维分割、虚拟切片显示、人机交互等功能,具有一定的后续研发参考价值。
This paper mainly studies the segmentation algorithm of Level Set and 3D reconstruction method for medical volume data through the analysis of lots of documents, which from the perspective of scientific visualization. The main work and the innovative achievements include:
1. Discussing the related concepts of the regular data sets, the visualization of medical data segmentation and 3D reconstruction technology, analysis of the current developments at home and abroad.
2. Considering the Level Set segmentation, a image segmentation algorithm combined the Fast-marching with the Narrow-band is developed, extract the organs accurately from the regular medical images. As a foundation of the next 3D reconstruction; Meanwhile, according to the district interested in the character and the environment in which its reasonable selection of seeds, which can solve the bottlenecks of seed selection.
3. According to the regular medical data, use the segmentation method proposed in this paper, a improved Marching Cubes methods based on segmentation and Ray Casting Volume Rending methods based on the color-filled are proposed. The experiments show that it can be better than the traditional method of reconstruction.
4. Research the VTK (Visualization Toolkit) and ITK (insight segmentation and registration Toolkit) which widely used at home and abroad, then achieve our segmentation algorithm and the 3D reconstruction methods with VC + +2005, given the key code.
5. Using the regular medical volume data and the Windows platform, a simple medical visualization system is developed. The system achieved the functions of 3D visualization, 2D segmentation, virtual slices human-computer interaction and etc. It has some value of Follow-up research.
引文
[1]刘济全.医学对象的可视化研究[博士论文][D].浙江:浙江大学,2002.10
[2]管伟光.体视化技术及其应用[M].北京:电子工业出版社,1998
[3]B.H.McCormick,T.A.DeFanti,M.D.Brown.Visualization in Scientific Computing[J].Computer Graphics,1987,21(6):1-14
[4]唐泽圣等.三维数据场可视化[M].北京:新华大学出版社,1999
[5]赵博文,杨园,姜卫香.断层超声显像技术在胎儿超声检查中的初步应用研究[J].中国超声医学杂志,2007,23(1):58-60
[6]田捷,包尚联,周明全.医学影像处理与分析[M].北京:电子工业出版社,2003.5
[7]H.Shen,C.R.Johnson.Semi-automatic image segmentation:A bimodal thresholding approach[J].Technical Report of Comp.Science,1994,19:132-144
[8]Mariano-Goulart D,Collet H,Kotzki PO.Semi-automatic segmentation of gated blood pool emission topographic images by watersheds:application to the determination of right and left ejection fractions[J].European Journal of Nuclear Medicine,1998,20:26-38
[9]罗希平,田捷.一种改进的交互式医学图像序列分割方法[J].电子学报,2003,31(1):29-32
[10]ZiKuan Chen,Sabee Molloi.Automatic 3D vascular tree construction in CT angiography[J].Computerized Medical Imaging and Graphics,2003,27:469-479
[11]Zhao Binsheng,Gordon Gamsu.Automatic detection of small lung nodules on CT utilizing alocal density maximum algorithm[J].Journal of Applied Clinical Medical Physics,2003,4(30):42-49
[12]李传富,周康源,陈曾胜,等.序列颅脑CT图像的颅腔内结构自动化分割[J].中国科学技术大学学报,2006,36(02):148-152
[13]杨大生,陈晖,邹丰美.一种有效的舌体自动化分割算法[J].计算机应用研究,2007,2:170-172
[14]王成儒,倪永婧.自动化技术中图像分割算法的评价[J].云南大学学报(自然科学版),2005,7(5A),333-336
[15]陈健,田捷,薛健,等.多速度函数水平集算法及在医学分割中的应用[J].软件学报,2007,18(04):842-849
[16]莫晓齐,王耀.基于遗传算法的熵算法在人类染色体图像的分割中的应[1]系统仿真学报,2006,18(07):1921-1925
[17]孙炜,王耀南.基于模糊小波神经网络的磁共振图像分割方法[J].中国生物医学工程学报,2006,25(3):267-282
[18]王成儒,倪永婧.基于精度依据准则的图像分割算法评价[J].微计算机信息,2006,22(13):248350
[19]罗涧辉,刘京南,余玲玲.三维物体形状检测精度分析与研究[J].电子工程师,2006,32(01):31-34
[20]陈以超,洪汉玉,王俊,等.一种鲁棒性的遥感图像地域分类新方法[J].计算机技术与发展,2007,17(08):136-139
[21]秦安,冯前进,陈武凡.知识引导几何动态轮廓线算法与MR心脏序列图像鲁棒分割[J].中国生物医学工程学报,2007,26:244-249
[22]Rafael C,Gonzalez,Richard E.Woods.Digital Image Processing Second Edition[J].Publishing House of Electronics Industry,2002,25(3):332-350
[23]QI Yong,LIU Feng-yu.Multi-Agent and Neural Network based Network Fault Diagnosis and 2-Dimensional Visualization[J].Journal of System Simulation,2005,17(9):2171-2174
[24]J.Kompenhans,J.Agocs,Y.Egami.Recent Developments of Image Based Measurement Methods for Application to Transonic Flows in Industrial Wind Tunnels[J].Chinese Journal of Aeronautics,2006,19(02):114-125
[25]张毓晋.图像分割[M].北京:科学出版社,2001
[26]Piertro Perona,Jitendra Malik.Scale space and edge detection using anisotropic diffusion[J].IEEE Trans.Pattren Anal.Machine Intell,1990,12(7):629-639
[27]Rudin,S.Oscher,E.Fatemi.Nonlinear total variation based noise removal algorithms[J].Physical,1992,60:259-260
[28]uis Alvarez,Pierre Louis Lions,Jean Michel Morel.Image selective smoothing and edge detection by nonlinear diffusion[J].SIAMJ.Numer.Anal,1992,29(3):845-856
[29]石澄贤,王元全,王平安.Min/Max曲率流实现图像去噪和增强分析[J].计算机工程与应用,2004,5:10-13
[30]Benjamin,B.Kimia,Kaleem Siddiqi.Geometric Heat Equation and Nonlinear Diffusion of Shapes and Images[J].Computer Vision and Image Understanding,1996,64(3):305-322
[31]D.Marr E.Hildreth.Theory of edge detection[J].Proc.R.Soc.Land B,1980,207,187-217
[32]Canny J.A Computational Approach to Edge Detection[J].IEEE Trans on Pattren Analysis and Machine Intelligence.1986,8(6):679-698
[33]Kass M,Witkin A,Terzopoulous D.Snakes:Active contour models.In:Brady IM,Rosenfield A eds.Proceedings of the 1st International conference on computer Vision.London[J].IEEE Computer Society Press,1987,1(4):259-268
[34]Mumford D,Shah J.Optimal approximations by piecewise smooth functions and associated variational problems[J].Communications on Pure and Applied Mathematics,1989,42:577-685
[35]Rafael C.Gonzalez,Richard E.Woods.Digital Image Processing Second Edition[J].Publishing House of Electronics Industry,2002,3(4):564-578
[36]J A Sethian.Numerical Algorithms for Propagating Interfaces:Hamilton-Jacobi,Equations and Conservation Laws[J].J.Diff Geom,1990,31(1):131-161
[37]S Osher,J A Sethian.Fronts.Propagating with Curvature-dependent Speed:Algorithms Based on Hamilton-Jacobi Formulations[J].Journal of Computational Physics,1988,79(1):12-49
[38]Malladi R,Sethian J,Vemuri B.Shape modeling with front propagation:a level set approach[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17(2):158-175
[39]Bertalmio,M.,Sapiro,G.,Randall,G.Region tracking on level-set methods[J].IEEE Transactions on Medical Imaging.1999,18(5):448-451
[40]Masouri,A-R.,Sirivong,B.,Konrad,J.Multiple motin segmentation with level sets[J].Proceedings of the SPIE,2000,3974:584-595
[41]Paragios N,Deriche R.Geodesic active regions and level set methods for motion estimation and tracking[J].Computer Vision and Image Understanding,2005,3:259-282
[42]Samson,C.,Blanc-Feraud,L.,Aubert,G.,et al.Level set model for image classification[J].International Journal of Computer Vision.,2000,40(3):187-197
[43]B.Kass,A.Witkin,D,Terzopoulos.Snakes:Active contour models[J].International Journal of Computer Vision,1987,1(4):321-331
[44]Chop D.Computing minimal surfaces via Level Set curvature flow[J].Journal of Computational Physics,1993,106:77-91
[45]J.A.Sethian,A fast marching level set method for monotonically advaJacing fronts[R].Proc.Nat.Acad.Sci,1996,93:1591-1798.
[46]刘扬,黄庆明,高文.自适应高斯混合模型球场检测算法及其在体育视频分析中的应用[J].计算机研究与发展,2006,43(7):1270-1215
[47]Malladi,R.Sethian.An O(N log(N))algorithm for shape modeling[J].Proceedings of the National Academy of Sciences,1996,193:9389-9392
[48]Lorensen,W.E.,Cline.Marching cubes:a high resolution 3D surface construction algorithm[J].Computer Graphics,1987,21(4):163-169
[49]田捷,等.实用图象分析与处理技术[M].北京:电子工业出版社,1994,12
[50]Will chroeder,Ken Martin,Bill Lorensen.The Visualization Toolkit.An Object -Oriented approach to 3D graphics[J].2002
[51]何晖光,田捷,赵明昌,杨骅,基于分割的三维医学图像表面重建算法[J].软件学报,2002,13(2):219-226
[52]Garay J A,Gopal I S.Call Preemption in Communication Networks[J].In:IEEE Transactions on Medical Imaging,1992,3:1043-1050
[53]刘丽冰.肿瘤靶向治疗中的计算机技术[R].全国第一届肿瘤靶向治疗大会,大会报告,2002
[54]张积仁.肿瘤靶向治疗的新进展[R].专题报告,2003,08
[2]管伟光.体视化技术及其应用[M].北京:电子工业出版社,1998
[3]B.H.McCormick,T.A.DeFanti,M.D.Brown.Visualization in Scientific Computing[J].Computer Graphics,1987,21(6):1-14
[4]唐泽圣等.三维数据场可视化[M].北京:新华大学出版社,1999
[5]赵博文,杨园,姜卫香.断层超声显像技术在胎儿超声检查中的初步应用研究[J].中国超声医学杂志,2007,23(1):58-60
[6]田捷,包尚联,周明全.医学影像处理与分析[M].北京:电子工业出版社,2003.5
[7]H.Shen,C.R.Johnson.Semi-automatic image segmentation:A bimodal thresholding approach[J].Technical Report of Comp.Science,1994,19:132-144
[8]Mariano-Goulart D,Collet H,Kotzki PO.Semi-automatic segmentation of gated blood pool emission topographic images by watersheds:application to the determination of right and left ejection fractions[J].European Journal of Nuclear Medicine,1998,20:26-38
[9]罗希平,田捷.一种改进的交互式医学图像序列分割方法[J].电子学报,2003,31(1):29-32
[10]ZiKuan Chen,Sabee Molloi.Automatic 3D vascular tree construction in CT angiography[J].Computerized Medical Imaging and Graphics,2003,27:469-479
[11]Zhao Binsheng,Gordon Gamsu.Automatic detection of small lung nodules on CT utilizing alocal density maximum algorithm[J].Journal of Applied Clinical Medical Physics,2003,4(30):42-49
[12]李传富,周康源,陈曾胜,等.序列颅脑CT图像的颅腔内结构自动化分割[J].中国科学技术大学学报,2006,36(02):148-152
[13]杨大生,陈晖,邹丰美.一种有效的舌体自动化分割算法[J].计算机应用研究,2007,2:170-172
[14]王成儒,倪永婧.自动化技术中图像分割算法的评价[J].云南大学学报(自然科学版),2005,7(5A),333-336
[15]陈健,田捷,薛健,等.多速度函数水平集算法及在医学分割中的应用[J].软件学报,2007,18(04):842-849
[16]莫晓齐,王耀.基于遗传算法的熵算法在人类染色体图像的分割中的应[1]系统仿真学报,2006,18(07):1921-1925
[17]孙炜,王耀南.基于模糊小波神经网络的磁共振图像分割方法[J].中国生物医学工程学报,2006,25(3):267-282
[18]王成儒,倪永婧.基于精度依据准则的图像分割算法评价[J].微计算机信息,2006,22(13):248350
[19]罗涧辉,刘京南,余玲玲.三维物体形状检测精度分析与研究[J].电子工程师,2006,32(01):31-34
[20]陈以超,洪汉玉,王俊,等.一种鲁棒性的遥感图像地域分类新方法[J].计算机技术与发展,2007,17(08):136-139
[21]秦安,冯前进,陈武凡.知识引导几何动态轮廓线算法与MR心脏序列图像鲁棒分割[J].中国生物医学工程学报,2007,26:244-249
[22]Rafael C,Gonzalez,Richard E.Woods.Digital Image Processing Second Edition[J].Publishing House of Electronics Industry,2002,25(3):332-350
[23]QI Yong,LIU Feng-yu.Multi-Agent and Neural Network based Network Fault Diagnosis and 2-Dimensional Visualization[J].Journal of System Simulation,2005,17(9):2171-2174
[24]J.Kompenhans,J.Agocs,Y.Egami.Recent Developments of Image Based Measurement Methods for Application to Transonic Flows in Industrial Wind Tunnels[J].Chinese Journal of Aeronautics,2006,19(02):114-125
[25]张毓晋.图像分割[M].北京:科学出版社,2001
[26]Piertro Perona,Jitendra Malik.Scale space and edge detection using anisotropic diffusion[J].IEEE Trans.Pattren Anal.Machine Intell,1990,12(7):629-639
[27]Rudin,S.Oscher,E.Fatemi.Nonlinear total variation based noise removal algorithms[J].Physical,1992,60:259-260
[28]uis Alvarez,Pierre Louis Lions,Jean Michel Morel.Image selective smoothing and edge detection by nonlinear diffusion[J].SIAMJ.Numer.Anal,1992,29(3):845-856
[29]石澄贤,王元全,王平安.Min/Max曲率流实现图像去噪和增强分析[J].计算机工程与应用,2004,5:10-13
[30]Benjamin,B.Kimia,Kaleem Siddiqi.Geometric Heat Equation and Nonlinear Diffusion of Shapes and Images[J].Computer Vision and Image Understanding,1996,64(3):305-322
[31]D.Marr E.Hildreth.Theory of edge detection[J].Proc.R.Soc.Land B,1980,207,187-217
[32]Canny J.A Computational Approach to Edge Detection[J].IEEE Trans on Pattren Analysis and Machine Intelligence.1986,8(6):679-698
[33]Kass M,Witkin A,Terzopoulous D.Snakes:Active contour models.In:Brady IM,Rosenfield A eds.Proceedings of the 1st International conference on computer Vision.London[J].IEEE Computer Society Press,1987,1(4):259-268
[34]Mumford D,Shah J.Optimal approximations by piecewise smooth functions and associated variational problems[J].Communications on Pure and Applied Mathematics,1989,42:577-685
[35]Rafael C.Gonzalez,Richard E.Woods.Digital Image Processing Second Edition[J].Publishing House of Electronics Industry,2002,3(4):564-578
[36]J A Sethian.Numerical Algorithms for Propagating Interfaces:Hamilton-Jacobi,Equations and Conservation Laws[J].J.Diff Geom,1990,31(1):131-161
[37]S Osher,J A Sethian.Fronts.Propagating with Curvature-dependent Speed:Algorithms Based on Hamilton-Jacobi Formulations[J].Journal of Computational Physics,1988,79(1):12-49
[38]Malladi R,Sethian J,Vemuri B.Shape modeling with front propagation:a level set approach[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1995,17(2):158-175
[39]Bertalmio,M.,Sapiro,G.,Randall,G.Region tracking on level-set methods[J].IEEE Transactions on Medical Imaging.1999,18(5):448-451
[40]Masouri,A-R.,Sirivong,B.,Konrad,J.Multiple motin segmentation with level sets[J].Proceedings of the SPIE,2000,3974:584-595
[41]Paragios N,Deriche R.Geodesic active regions and level set methods for motion estimation and tracking[J].Computer Vision and Image Understanding,2005,3:259-282
[42]Samson,C.,Blanc-Feraud,L.,Aubert,G.,et al.Level set model for image classification[J].International Journal of Computer Vision.,2000,40(3):187-197
[43]B.Kass,A.Witkin,D,Terzopoulos.Snakes:Active contour models[J].International Journal of Computer Vision,1987,1(4):321-331
[44]Chop D.Computing minimal surfaces via Level Set curvature flow[J].Journal of Computational Physics,1993,106:77-91
[45]J.A.Sethian,A fast marching level set method for monotonically advaJacing fronts[R].Proc.Nat.Acad.Sci,1996,93:1591-1798.
[46]刘扬,黄庆明,高文.自适应高斯混合模型球场检测算法及其在体育视频分析中的应用[J].计算机研究与发展,2006,43(7):1270-1215
[47]Malladi,R.Sethian.An O(N log(N))algorithm for shape modeling[J].Proceedings of the National Academy of Sciences,1996,193:9389-9392
[48]Lorensen,W.E.,Cline.Marching cubes:a high resolution 3D surface construction algorithm[J].Computer Graphics,1987,21(4):163-169
[49]田捷,等.实用图象分析与处理技术[M].北京:电子工业出版社,1994,12
[50]Will chroeder,Ken Martin,Bill Lorensen.The Visualization Toolkit.An Object -Oriented approach to 3D graphics[J].2002
[51]何晖光,田捷,赵明昌,杨骅,基于分割的三维医学图像表面重建算法[J].软件学报,2002,13(2):219-226
[52]Garay J A,Gopal I S.Call Preemption in Communication Networks[J].In:IEEE Transactions on Medical Imaging,1992,3:1043-1050
[53]刘丽冰.肿瘤靶向治疗中的计算机技术[R].全国第一届肿瘤靶向治疗大会,大会报告,2002
[54]张积仁.肿瘤靶向治疗的新进展[R].专题报告,2003,08