基于CT图像序列的血管结构三维重建方法研究
|
摘要
医学图像三维重建,是计算机图形学和数字图像处理技术在生物医学工程中的重要应用。医学三维重建技术已经成为医院诊断、治疗的重要依据和手段,在医学诊断、手术规划、模拟仿真、整形及假肢外科、解剖教学等方面都有重要的应用。
本文对互相缠绕,结构复杂的血管进行三维重建。先用面绘制方法中的移动立方体经典算法进行了重建,针对管状结构,本文又提出了一种自动提取三维中心线并且估计其半径来描绘血管物体的方法。
首先,面绘制方法存在不能反映整个原始数据场的全貌及细节的问题,本文采用提取三维中心线并且估计其半径来描绘血管物体。本文先对切片图像进行预处理,以得到物体间对比最大化,然后用带标记控制的分水岭算法分割互相缠绕的血管。在最优路径上的有最小花费的点定义为前景标记,分割区域的质心用来调整搜索到的中心线点。这种方法可以快速准确的提取各种情况的中心线,处理诸如横渡截面,重合分割面的复杂血管结构。
其次,提取血管的三维中心线。提取时,包括两个步骤:先用三维线性过滤方法为每一个点分配一个相似度测量,然后在每一个点内搜索区域本文寻求一个最优路径。
再次,针对文献中的方法估计血管不准的问题,我们在估计半径时,先对分割后的区域进行裁剪,然后对裁剪过分的部分进行合并,最后对半径进行估计。这样估计出的半径能够全部覆盖血管的主要信息,准确的表达出血管结构。本文在描绘立体图时用椭圆来描绘血管的形状。
最后,针对现有方法半自动需要手工操纵的缺点,本文提出一种全自动的方法。
Three-dimensional reconstruction of medical images is a multi-disciplinary subject. It is an important application of computer graphics and image processing in biomedicine engineering, which has become a helpful means for clinic diagnosis and treatments. Three-dimensional reconstruction and visualization of medical images are widely used in diagnostic, surgery planning and simulating, plastic and artificial limb surgery, and teaching in anatomy.
This paper focuses on three-dimensional reconstruction of complicated vessels structures such as cross-over sections and attaching segments. The principle and implementation of the Marching Cubes, a well-known surface extraction algorithm, are demonstrated, so we propose an automated three-dimensional centerline extraction and vessel radius estimation for three-dimensional reconstruction tool to assist in this task.
First, due to Marching Cubes cannot reflect the raw data, we propose a three dimensional centerline extraction and vessel radius estimation for three dimensional reconstruction tool. The contrast of the objects is maximized by preprocessing, and an marker-controlled watershed algorithm is used to segment different vessel objects in order to distinguish axons that are attaching together. The points on the optimal paths with the minimum cost values are regarded as the foreground markers. The centroids of the segmented regions are used to adjust the searched centerline points using DP. The proposed method can rapidly and accurately extract multiple vessel centerlines and can handle.
Second, we propose a highly automated three-dimensional centerline extraction tool to assist in this task. It consists of two steps. In the first step, every point in the image stack is assigned a similarity measurement to the three-dimensional line structure using the three-dimensional line filtering method. In the second step, we search for an optimal path for each point within the search region on the current slice to the centerline points on the previous slice.
Third, because the inaccuracy of estimating the radius of the vessels in the literature method, we cut and merge the regions after segmentation to estimate the radius of the vessels for three-dimensional vessel reconstruction. They cover all the key messages and accurate expression of the vessels structures. And then we describe the blood vessels with an oval shape.
At last, because the method is semiautomatic, we design a fully automated algorithm.
本文对互相缠绕,结构复杂的血管进行三维重建。先用面绘制方法中的移动立方体经典算法进行了重建,针对管状结构,本文又提出了一种自动提取三维中心线并且估计其半径来描绘血管物体的方法。
首先,面绘制方法存在不能反映整个原始数据场的全貌及细节的问题,本文采用提取三维中心线并且估计其半径来描绘血管物体。本文先对切片图像进行预处理,以得到物体间对比最大化,然后用带标记控制的分水岭算法分割互相缠绕的血管。在最优路径上的有最小花费的点定义为前景标记,分割区域的质心用来调整搜索到的中心线点。这种方法可以快速准确的提取各种情况的中心线,处理诸如横渡截面,重合分割面的复杂血管结构。
其次,提取血管的三维中心线。提取时,包括两个步骤:先用三维线性过滤方法为每一个点分配一个相似度测量,然后在每一个点内搜索区域本文寻求一个最优路径。
再次,针对文献中的方法估计血管不准的问题,我们在估计半径时,先对分割后的区域进行裁剪,然后对裁剪过分的部分进行合并,最后对半径进行估计。这样估计出的半径能够全部覆盖血管的主要信息,准确的表达出血管结构。本文在描绘立体图时用椭圆来描绘血管的形状。
最后,针对现有方法半自动需要手工操纵的缺点,本文提出一种全自动的方法。
Three-dimensional reconstruction of medical images is a multi-disciplinary subject. It is an important application of computer graphics and image processing in biomedicine engineering, which has become a helpful means for clinic diagnosis and treatments. Three-dimensional reconstruction and visualization of medical images are widely used in diagnostic, surgery planning and simulating, plastic and artificial limb surgery, and teaching in anatomy.
This paper focuses on three-dimensional reconstruction of complicated vessels structures such as cross-over sections and attaching segments. The principle and implementation of the Marching Cubes, a well-known surface extraction algorithm, are demonstrated, so we propose an automated three-dimensional centerline extraction and vessel radius estimation for three-dimensional reconstruction tool to assist in this task.
First, due to Marching Cubes cannot reflect the raw data, we propose a three dimensional centerline extraction and vessel radius estimation for three dimensional reconstruction tool. The contrast of the objects is maximized by preprocessing, and an marker-controlled watershed algorithm is used to segment different vessel objects in order to distinguish axons that are attaching together. The points on the optimal paths with the minimum cost values are regarded as the foreground markers. The centroids of the segmented regions are used to adjust the searched centerline points using DP. The proposed method can rapidly and accurately extract multiple vessel centerlines and can handle.
Second, we propose a highly automated three-dimensional centerline extraction tool to assist in this task. It consists of two steps. In the first step, every point in the image stack is assigned a similarity measurement to the three-dimensional line structure using the three-dimensional line filtering method. In the second step, we search for an optimal path for each point within the search region on the current slice to the centerline points on the previous slice.
Third, because the inaccuracy of estimating the radius of the vessels in the literature method, we cut and merge the regions after segmentation to estimate the radius of the vessels for three-dimensional vessel reconstruction. They cover all the key messages and accurate expression of the vessels structures. And then we describe the blood vessels with an oval shape.
At last, because the method is semiautomatic, we design a fully automated algorithm.
引文
1唐泽圣.三维数据场可视化.北京:清华大学出版社, 1999.
2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.
3张绍祥,刘正津,谭立文,等.首例中国数字化可视人体完成.第三军医大学学报, 2005, 24(10): 1231-1232.
4田捷,赵明昌,何晖光.集成化医学影像算法平台理论与实践.北京:清华大学出版社, 2004.
5邢英杰,张少华,刘晓冰.虚拟手术系统技术现状.计算机工程与应用, 2004, 7:88-90.
6王子罡,唐泽圣,王田苗,等.基于虚拟现实的计算机辅助立体定向神经外科手术系统.计算机学报, 2000, 23(9): 931-937.
7阎丽霞,王健宁,石教英.虚拟手术中的软组织变形仿真研究.系统仿真学报, 2001, 13(5): 294-296.
8 W. Schroeder, K. Martin, and W. Lorensen. The Visualization Toolkit: An Object-Oriented Approach to 3D Graphics, 2nd ed., Prentice-Hall, Old Tappan, N.J., 1998.
9 William J. Schroeder, Lisa S. Avila, William Hoffman. Visualizing with VTK: A torial. IEEE Computer Graphics and Applications, September- October 2000, Vol. 20: 20-27.
10 Luis Ib’aenez, Will Schroeder, Lydia Ng, et al. The ITK Software Guide, 2nd ed., Insight Software Consortium, 2005.
11 Mingchang Zhao, Jie Tian, Xun Zhu, et al. The Design and Implementation of a C++ Toolkit for Integrated Medical Image Processing and Analyzing. Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display, Proceedings of SPIE Vol. 5367: 39-47.
12 Kevin M. McNeill, Masakazu Osada, Ralph Martinez, et al. Evaluation of the ACR-NEMA Standard for Communications in Digital Radiology. IEEE Transactions on Medical Imaging. 1990, Vol. 9. No. 3: 281-289.
13 W. Lorenson, H. Cline. Marching cubes: A high resolution 3d surface construction algorithm[J]. Computer Graphics. July 1987.
14蒋丹,应用体绘制技术实现三维数据场的实现.上海交通大学博士学位论文.
15 Levoy M. 1988. Display of Surfaces from Volume Data. IEEE Computer Graphics and Application, 8(3): 29-37.
16 W. Heidrich, M. McCool, J. Stevens. Interactive Maximum Projection Volume Rendering[J]. Proc. IEEE Vis.’95, 1995: 11-18.
17 W. E. Lorensen, H. E. Cline. Marching Cubes: A High Resolution 3D Surface Construction Algorithm. SIGGRAPH Proc., 1987, Vol. 21: 163-169.
18 Durst. M. J. Letters: Additional Reference to‘marching cubes’. Computer Graphics. 1988, Vol.22, No. 2.
19 Gregory M. Neilson, Bernd Hamann. The Asymptotic Decider: Resolving the Ambiguity in Marching Cubes. Proc. IEEE Visualization, October 1991: 83-91
20 1 Sergey V. Matveyev. Marching cubes: surface complexity measure. Part of the IS&T/SPIE Conference on Visual Data Exploration and Analysis. January 1999, SPIE Vol. 3643: 220-225.
21 Adriano Lopes, Ken Brodlie. Improving the Robustness and Accuracy of the Marching Cubes Algorithm for Isosurfacing. IEEE Transactions on Visualization and Computer Graphics, January-March 2004, Vol. 9, No. 1: 16-29.
22 Gregory M. Neilson. On Marching Cubes. IEEE Transactions on Visualization and Computer Graphics, July-September 2004, Vol. 9, No. 3: 283-297.
23 Chandrajit L. Bajaj, Valerio Pascucci, Daniel R. Schikore. Fast Isocontouring For Improved Interactivity. IEEE 2006 Symposium on Volume Visualization: 39-46.
24 Yarden Livnat, Han-Wei Shen, Christopher R. Johnson. A Near Optimal Isosurface Extraction Algorithm Using the Span Space. IEEE Transactions on Visualization and Computer Graphics,January-March 2006, Vol. 2, No. 1: 73-84.
25 Takayuki Itoh, Yasushi Yamaguchi, Koji Koyamada. Fast Isosurface Generation Using the Volume Thinning Algorithm. IEEE Transactions on Visualization and Computer Graphics,January-March 2006, Vol. 7, No. 1: 32-46.
26 Jane Wilhelms, Allen Van Gelder. Octrees for Faster Isosurface Generation. ACM Transactions on Graphics, July 1992, Vol. 11, No. 3: 201-227.
27 Doi. A, Koide A. An Efficient Method of Triangulating Equi-Valued Surfaces by Using Tetrahedral Cells. IEICE Transactions, E74 (1): 214-224.
28 Chin-Feng Lin, Don-Lin Yang, Yeh-Ching Chung. A Marching Voxels Method for Surface Rendering of Volume Data. IEEE Transactions on Visualization and Computer Graphics, 2005,Vol. 7: 306-313.
29 H. E. Cline, W. E. Lorensen. Two Algorithms for Three-dimensional Reconstruction of Tomograms. Medical Physics, 1988, 15 (3): 320-327.
30 Meijering E, Jacob M, Sarria J-CF, Steiner P, Hirling H, Unser M. Design andvalidation of a tool for neurite tracing and analysis in fluorescence microscopy images. Cytometry 2004; 58A(2): 167-176.
31 Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man,and Cybernetics 1979; 9(1): 62-66.
32 Zhang Y, Zhou X, Degterev A, Lipinski M, Adjeroh D, Yuan J, Wong STC. Automated neurite extraction using dynamic programming for high- throughput screening of neuron-based assays. NeuroImage 007; doi: 10.1016/ j.neuroimage. 2007. 01. 014.
33 Haralick RM, Watson LT, Laffey TJ. The topographic primal sketch. The International Journal of Robotics Research 1983; 2: 50-72.
34 Koller TM, Gerig G, Szekely G, Dettwiler D. Multiscale detection of curvilinear structures in 2-D and 3-D image data. Fifth International Conference on Computer Vision; 1995.
35 Lorenz C, Carlsen I-C, Buzug TM, Fassnacht C, Weese J. Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2-D and 3-D medical images. Proceedings of the First Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery; 1997.
36 Sato Y, Araki T, Hanayama M, Naito H, Tamura S. A viewpoint determination system for stenosis diagnosis and quantification in coronary angiographic image acquisition. IEEE Transactions on Medical Imaging 1998; 17: 121-137.
37 Sato Y, Nakajima S, Shiraga N, Atsumi H, Yoshida S, Koller T, Gerig G, Kikinis R. Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images.Medical Image Analysis 1998;2:143-168.
38 Steger C. An unbiased detector of curvilinear structure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998; 20(2): 113-125.
39 Steger C. Extracting curvilinear structures: A differential geometric approach. In: Buxton BF, Cipolla R,editors. Lecture Notes in Computer Science; 1996 Cambridge, UK. Springer. p 630-641. (Lecture Notes in Computer Science).
40 C. Lorenz, I.-C. Carlsen, T. M. Buzug, C. Fassnacht, and J. Weese,“Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2d and 3d medical images,”in CVPRMed-MRCAS, 1997, pp. 233–242.
41 P.-E. Danielsson and Q. Lin,“Efficient detection of second degree variations in 2d and 3d images,”Journal of Visual Communication and Image Representation, vol. 12, pp. 255–305, 2004.
42 G. Gerig, Th. Koller, G. Szekely, Ch. Brechbfihler & O. Kfibler, Segmentation andSymbolic Description of Cerebral Vessel Structure based on MR Angiography Volume Data, Computer Assisted Radiology, Proceedings of the International Symposium.CAR'93 Berlin 359 (1993).
43 Th. M. Koller, G. Gerig, G. Szekely & D. Dettwiler, Multiscale Detection of Curvilinear Structures in 2-D and 3D Image Data, Fifth International Conference on Computer Vision (June 20-23), Cambridge, MA, USA, 864 (1995).
44 Th. M. Koller, From Data to Information: Segmentation, Description and Analysis of the Cerebral Vascularity (ETH Phi) 11367), Dissertation, Swiss Federal Insitute of Technology, Zfirich, 1996.
45 Zhang Y, Zhou X, Degterev A, Lipinski M, Adjeroh D, Yuan J, Wong STC. Automated neurite extraction using dynamic programming for high-throughput screening of neuron-based assays. NeuroImage 2007; doi:10.1016/j.neuroimage. 2007. 01. 014.
2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.
3张绍祥,刘正津,谭立文,等.首例中国数字化可视人体完成.第三军医大学学报, 2005, 24(10): 1231-1232.
4田捷,赵明昌,何晖光.集成化医学影像算法平台理论与实践.北京:清华大学出版社, 2004.
5邢英杰,张少华,刘晓冰.虚拟手术系统技术现状.计算机工程与应用, 2004, 7:88-90.
6王子罡,唐泽圣,王田苗,等.基于虚拟现实的计算机辅助立体定向神经外科手术系统.计算机学报, 2000, 23(9): 931-937.
7阎丽霞,王健宁,石教英.虚拟手术中的软组织变形仿真研究.系统仿真学报, 2001, 13(5): 294-296.
8 W. Schroeder, K. Martin, and W. Lorensen. The Visualization Toolkit: An Object-Oriented Approach to 3D Graphics, 2nd ed., Prentice-Hall, Old Tappan, N.J., 1998.
9 William J. Schroeder, Lisa S. Avila, William Hoffman. Visualizing with VTK: A torial. IEEE Computer Graphics and Applications, September- October 2000, Vol. 20: 20-27.
10 Luis Ib’aenez, Will Schroeder, Lydia Ng, et al. The ITK Software Guide, 2nd ed., Insight Software Consortium, 2005.
11 Mingchang Zhao, Jie Tian, Xun Zhu, et al. The Design and Implementation of a C++ Toolkit for Integrated Medical Image Processing and Analyzing. Medical Imaging 2004: Visualization, Image-Guided Procedures, and Display, Proceedings of SPIE Vol. 5367: 39-47.
12 Kevin M. McNeill, Masakazu Osada, Ralph Martinez, et al. Evaluation of the ACR-NEMA Standard for Communications in Digital Radiology. IEEE Transactions on Medical Imaging. 1990, Vol. 9. No. 3: 281-289.
13 W. Lorenson, H. Cline. Marching cubes: A high resolution 3d surface construction algorithm[J]. Computer Graphics. July 1987.
14蒋丹,应用体绘制技术实现三维数据场的实现.上海交通大学博士学位论文.
15 Levoy M. 1988. Display of Surfaces from Volume Data. IEEE Computer Graphics and Application, 8(3): 29-37.
16 W. Heidrich, M. McCool, J. Stevens. Interactive Maximum Projection Volume Rendering[J]. Proc. IEEE Vis.’95, 1995: 11-18.
17 W. E. Lorensen, H. E. Cline. Marching Cubes: A High Resolution 3D Surface Construction Algorithm. SIGGRAPH Proc., 1987, Vol. 21: 163-169.
18 Durst. M. J. Letters: Additional Reference to‘marching cubes’. Computer Graphics. 1988, Vol.22, No. 2.
19 Gregory M. Neilson, Bernd Hamann. The Asymptotic Decider: Resolving the Ambiguity in Marching Cubes. Proc. IEEE Visualization, October 1991: 83-91
20 1 Sergey V. Matveyev. Marching cubes: surface complexity measure. Part of the IS&T/SPIE Conference on Visual Data Exploration and Analysis. January 1999, SPIE Vol. 3643: 220-225.
21 Adriano Lopes, Ken Brodlie. Improving the Robustness and Accuracy of the Marching Cubes Algorithm for Isosurfacing. IEEE Transactions on Visualization and Computer Graphics, January-March 2004, Vol. 9, No. 1: 16-29.
22 Gregory M. Neilson. On Marching Cubes. IEEE Transactions on Visualization and Computer Graphics, July-September 2004, Vol. 9, No. 3: 283-297.
23 Chandrajit L. Bajaj, Valerio Pascucci, Daniel R. Schikore. Fast Isocontouring For Improved Interactivity. IEEE 2006 Symposium on Volume Visualization: 39-46.
24 Yarden Livnat, Han-Wei Shen, Christopher R. Johnson. A Near Optimal Isosurface Extraction Algorithm Using the Span Space. IEEE Transactions on Visualization and Computer Graphics,January-March 2006, Vol. 2, No. 1: 73-84.
25 Takayuki Itoh, Yasushi Yamaguchi, Koji Koyamada. Fast Isosurface Generation Using the Volume Thinning Algorithm. IEEE Transactions on Visualization and Computer Graphics,January-March 2006, Vol. 7, No. 1: 32-46.
26 Jane Wilhelms, Allen Van Gelder. Octrees for Faster Isosurface Generation. ACM Transactions on Graphics, July 1992, Vol. 11, No. 3: 201-227.
27 Doi. A, Koide A. An Efficient Method of Triangulating Equi-Valued Surfaces by Using Tetrahedral Cells. IEICE Transactions, E74 (1): 214-224.
28 Chin-Feng Lin, Don-Lin Yang, Yeh-Ching Chung. A Marching Voxels Method for Surface Rendering of Volume Data. IEEE Transactions on Visualization and Computer Graphics, 2005,Vol. 7: 306-313.
29 H. E. Cline, W. E. Lorensen. Two Algorithms for Three-dimensional Reconstruction of Tomograms. Medical Physics, 1988, 15 (3): 320-327.
30 Meijering E, Jacob M, Sarria J-CF, Steiner P, Hirling H, Unser M. Design andvalidation of a tool for neurite tracing and analysis in fluorescence microscopy images. Cytometry 2004; 58A(2): 167-176.
31 Otsu N. A threshold selection method from gray-level histograms. IEEE Transactions on Systems, Man,and Cybernetics 1979; 9(1): 62-66.
32 Zhang Y, Zhou X, Degterev A, Lipinski M, Adjeroh D, Yuan J, Wong STC. Automated neurite extraction using dynamic programming for high- throughput screening of neuron-based assays. NeuroImage 007; doi: 10.1016/ j.neuroimage. 2007. 01. 014.
33 Haralick RM, Watson LT, Laffey TJ. The topographic primal sketch. The International Journal of Robotics Research 1983; 2: 50-72.
34 Koller TM, Gerig G, Szekely G, Dettwiler D. Multiscale detection of curvilinear structures in 2-D and 3-D image data. Fifth International Conference on Computer Vision; 1995.
35 Lorenz C, Carlsen I-C, Buzug TM, Fassnacht C, Weese J. Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2-D and 3-D medical images. Proceedings of the First Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery; 1997.
36 Sato Y, Araki T, Hanayama M, Naito H, Tamura S. A viewpoint determination system for stenosis diagnosis and quantification in coronary angiographic image acquisition. IEEE Transactions on Medical Imaging 1998; 17: 121-137.
37 Sato Y, Nakajima S, Shiraga N, Atsumi H, Yoshida S, Koller T, Gerig G, Kikinis R. Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images.Medical Image Analysis 1998;2:143-168.
38 Steger C. An unbiased detector of curvilinear structure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1998; 20(2): 113-125.
39 Steger C. Extracting curvilinear structures: A differential geometric approach. In: Buxton BF, Cipolla R,editors. Lecture Notes in Computer Science; 1996 Cambridge, UK. Springer. p 630-641. (Lecture Notes in Computer Science).
40 C. Lorenz, I.-C. Carlsen, T. M. Buzug, C. Fassnacht, and J. Weese,“Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2d and 3d medical images,”in CVPRMed-MRCAS, 1997, pp. 233–242.
41 P.-E. Danielsson and Q. Lin,“Efficient detection of second degree variations in 2d and 3d images,”Journal of Visual Communication and Image Representation, vol. 12, pp. 255–305, 2004.
42 G. Gerig, Th. Koller, G. Szekely, Ch. Brechbfihler & O. Kfibler, Segmentation andSymbolic Description of Cerebral Vessel Structure based on MR Angiography Volume Data, Computer Assisted Radiology, Proceedings of the International Symposium.CAR'93 Berlin 359 (1993).
43 Th. M. Koller, G. Gerig, G. Szekely & D. Dettwiler, Multiscale Detection of Curvilinear Structures in 2-D and 3D Image Data, Fifth International Conference on Computer Vision (June 20-23), Cambridge, MA, USA, 864 (1995).
44 Th. M. Koller, From Data to Information: Segmentation, Description and Analysis of the Cerebral Vascularity (ETH Phi) 11367), Dissertation, Swiss Federal Insitute of Technology, Zfirich, 1996.
45 Zhang Y, Zhou X, Degterev A, Lipinski M, Adjeroh D, Yuan J, Wong STC. Automated neurite extraction using dynamic programming for high-throughput screening of neuron-based assays. NeuroImage 2007; doi:10.1016/j.neuroimage. 2007. 01. 014.