DSA图像三维重建中的二维信息处理及三维表达
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摘要
数字减影血管造影技术(Digital Subtraction Angiography,简称DSA)是一种广泛使用的血管可视化技术,在临床已应用20多年,是血管疾病无创诊断与介入治疗的重要依据,广泛应用于X射线序列成像中的血管的可视化系统中。
本文主要研究数字减影血管造影三维重建过程中的血管分割、中轴提取以及血管的三维表达问题。
血管分割是DSA三维重建的基础,本文针对减影效果较好的DSA图像提出了知识导引的血管造影图像局部阈值分割方法。在血管直径先验知识的指导下将图像分成适当大小的若干子块,引入血管存在性判据,采用有重叠的图像分块技术对各子块进行局部阈值分割,再融合各子块分割结果。针对脑部和肝部DSA图像进行实验后,结果表明本文所提出的方法可以快速有效的分割出血管结构。
中轴提取方面提出了基于多尺度Gabor滤波的方法对血管进行跟踪提取中轴。Gabor滤波器灵活的方位带宽和频率带宽使得血管中轴得到很好的增强,在增强后的响应图上人工选取各分支起点和终点,用最短路径法进行跟踪。对血管进行半自动分支跟踪可以解决血管的交叉,临近血管的区分,重叠等问题,使提取的中轴清晰准确。
血管三维表达方面,先简短的介绍了三维表达前所必须的步骤:两个不同视角图的配准和骨架重建,再重点介绍基于广义圆柱模型(Generalized Cylinder)的血管三维表达。其中包括提取血管三维重建半径,对三维血管中轴进行平滑和为了得到血管横截面上圆周的三维坐标所进行的空间直角坐标变换。该方法简单效率高,能很好的显示完整的三维血管。
最后是通过血管的表面绘制方法,利用VTK对血管进行三维显示。此方法绘制速度快,显示结果逼真有立体感。
Digital subtraction angiography (DSA) is a widely used technique for the visualization of blood vessels in the human body and has been used in clinic for more than 20 years,which has significant values in the diagnosis and therapy of all kinds of vessel diseases.
In this paper we mostly research vessel segmentation, vessel centerline extraction and the three dimensional representation of vessels in the three dimensional reconstruction .
Vessel segmentation is the base of 3d reconstruction on Digital Subtraction Angiography images. In this paper we propose local thresholding approach of vessel segmentation based on knowledge for DSA images. The original DSA image is divided into several appropriate subimages according to a priori knowledge of the diameter of vessels. Then we apply local thresholding techniques to choose an optimal threshold for every subimage respectively. Finally an overall binarization of the original image is achieved by syncretizing the thresholded subimages. We do experiments on cerebral and hepatic DSA images, showing that our proposed methods yield satisfactory binary results efficiently.
In the aspect of vessel centerline extraction, we propose multiscale vessel tracking using Gabor filters to extract and track vessels. The flexible frequency bands and enhancement effects of Gabor filters,are fully utilized to enhance centerlines of the blood vessels in various size. Than we choose a source and a goal in the enhancement response image to track the vessel by minimum cost path .This method should be able to cope with close proximity or crossing of vessels as well as with varying vessel widths including large stenoses and severe imaging artifacts.
In the three dimensional representation of vessels, we firstly discuss the registration of two views and the skeleton reconstruction, than mainly introduce three dimensional representation of vessels based on the model of generalized cylinders. The second step includes acquirement of the three dimensional radius of vessels, smoothing centerlines and space orthogonal coordinate transformation. The proposed method is simple and efficient, which can show full three dimensional vessels.
Finally, surface rendering is used to show the whole three dimensional vessels by VTK. This method is fast and show living result.
本文主要研究数字减影血管造影三维重建过程中的血管分割、中轴提取以及血管的三维表达问题。
血管分割是DSA三维重建的基础,本文针对减影效果较好的DSA图像提出了知识导引的血管造影图像局部阈值分割方法。在血管直径先验知识的指导下将图像分成适当大小的若干子块,引入血管存在性判据,采用有重叠的图像分块技术对各子块进行局部阈值分割,再融合各子块分割结果。针对脑部和肝部DSA图像进行实验后,结果表明本文所提出的方法可以快速有效的分割出血管结构。
中轴提取方面提出了基于多尺度Gabor滤波的方法对血管进行跟踪提取中轴。Gabor滤波器灵活的方位带宽和频率带宽使得血管中轴得到很好的增强,在增强后的响应图上人工选取各分支起点和终点,用最短路径法进行跟踪。对血管进行半自动分支跟踪可以解决血管的交叉,临近血管的区分,重叠等问题,使提取的中轴清晰准确。
血管三维表达方面,先简短的介绍了三维表达前所必须的步骤:两个不同视角图的配准和骨架重建,再重点介绍基于广义圆柱模型(Generalized Cylinder)的血管三维表达。其中包括提取血管三维重建半径,对三维血管中轴进行平滑和为了得到血管横截面上圆周的三维坐标所进行的空间直角坐标变换。该方法简单效率高,能很好的显示完整的三维血管。
最后是通过血管的表面绘制方法,利用VTK对血管进行三维显示。此方法绘制速度快,显示结果逼真有立体感。
Digital subtraction angiography (DSA) is a widely used technique for the visualization of blood vessels in the human body and has been used in clinic for more than 20 years,which has significant values in the diagnosis and therapy of all kinds of vessel diseases.
In this paper we mostly research vessel segmentation, vessel centerline extraction and the three dimensional representation of vessels in the three dimensional reconstruction .
Vessel segmentation is the base of 3d reconstruction on Digital Subtraction Angiography images. In this paper we propose local thresholding approach of vessel segmentation based on knowledge for DSA images. The original DSA image is divided into several appropriate subimages according to a priori knowledge of the diameter of vessels. Then we apply local thresholding techniques to choose an optimal threshold for every subimage respectively. Finally an overall binarization of the original image is achieved by syncretizing the thresholded subimages. We do experiments on cerebral and hepatic DSA images, showing that our proposed methods yield satisfactory binary results efficiently.
In the aspect of vessel centerline extraction, we propose multiscale vessel tracking using Gabor filters to extract and track vessels. The flexible frequency bands and enhancement effects of Gabor filters,are fully utilized to enhance centerlines of the blood vessels in various size. Than we choose a source and a goal in the enhancement response image to track the vessel by minimum cost path .This method should be able to cope with close proximity or crossing of vessels as well as with varying vessel widths including large stenoses and severe imaging artifacts.
In the three dimensional representation of vessels, we firstly discuss the registration of two views and the skeleton reconstruction, than mainly introduce three dimensional representation of vessels based on the model of generalized cylinders. The second step includes acquirement of the three dimensional radius of vessels, smoothing centerlines and space orthogonal coordinate transformation. The proposed method is simple and efficient, which can show full three dimensional vessels.
Finally, surface rendering is used to show the whole three dimensional vessels by VTK. This method is fast and show living result.
引文
[1] Shechter G., Devemay F. Three-Dimensional Motion Tracking of Coronary Arteries in Biplane Cineangiograms. IEEE Transactions on Medical Imaging, 2003, 22(4):493-503.
[2] Keith M. Andress. Evidential Reconstruction of Vessel Trees from Rotational Angiograms. IEEE Transactions on Medical Imaging, 1998, 3: 385-389.
[3] R. T. Ritchings, A.C.F. Colchester. Detection of abnormalities on carotid angiograms. Pattern Recognition Letters, 1986, 4(5):367-374
[4] B. D. Thackray, A. C, Nelson. Semi-automatic segmentation of vascular network images using a rotating structuring element (ROSE) with mathematical morphology and dual feature thresholding. IEEE Transactions on Medical Imagine, 1993, 12(3):385- 392
[5] 1. Liu, Y Sun. Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme. IEEE Transaction on medical imaging, 1993, 12(2):334- 341
[6] I. Liu, Y Sun. Recursive tracking of vascular trees in angiograms using a detection-deletion scheme. IEEE Workshop on Mathematical Methods in Biomedical Image Analysis , 1990, 4:169-170
[7] S. Aylward, S. Pizer, E. Bullit, and D. Eberl. Intensity ridge and widths for tubular object segmentation and description. IEEE Workshop on Mathematical Methods in Biomedical Image Analysis 1996, 2:131-138
[8] D. J. Stevenson, L. D. R. Smith, G. Robison. Working towards the automatic detection of blood vessels in x-ray angiograms. Pattern recognition letters, 1994, 16(1):295-301
[9] A. K. Klein, F. Lee, A. Amini. Quantitative coronary angiography withdeformable spline models. IEEE Transactions on medical imaging, 1997, 16(5): 468-482
[10] Hyun Wook Park, Todd Schoepfline, and Yongmin Kim. Active contour model with gradient directional information: directional snake. IEEE Transactions on Circuits and Systems for Video Technology, 2001, 11(2):252-256
[11] Amit Chakraboty, Lawrence H. Staib. Deformable Boundary Finding in Medical images by integrating gradient and region information. IEEE Transactions on Medical Imaging, Dec. 1996, 15(6):859-870
[12] S. A. Stansfiled. ANGY: A rule-Based expert System for automatic segmentation of coronary vessels from digital subtracted angiograms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(2):188-199
[13] C. Smets, G. Verbeeck, P Suetens. A knowledge-based system for the delineation of blood vessels on subtraction angiograms. Pattern Recognition Letters,1998, 8(2):113-121
[14] R. Nekovei, Y Sun. Back-propagation network and its configuration for blood vessel detection in angiograms. IEEE Transactions on neural networks. 1995, 6(1):64-72
[15] V Caselles, F. Cate, T. Coll, F. Dibos. A geometric model for active contours in image processing, Numerische Mathematik. 1993, 66(1):1-31
[16] R. Malladi, J. A. Sethian, and B.C. Vemuri. Shape modeling with front propagation: A level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(2):158-175
[17] Kirbas, C. and Quek, F. A Review of Vessel Extraction Techniques and Algorithms. ACM Computing Surveys (CSUR), June, 2004, 36(2): 81-121.
[18] Mehmet Sezgin, Bulent Sankur. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging ,2004, 13(1): 146–168
[19] Xiaoyi Jiang, Daniel Mojon. Adaptive Local Thresholding by Verification-Based Multithreshold Probing with Application to Vessel Detection in Retinal Images. IEEE Trans. Pattern analysis and Machine Intelligence, January 2003, 25(1): 131-137
[20] N. R. Pal, S. K. Pal. A review on image segmentation techniques. Pattern Recognition. 1993, 26(9): 1227-1249
[21] C.k.Chow, T.Kaneko. Automatic boundary of left ventricle from cineangiograms. Computers and Biomedical Research, 1972, 5(4):388-410
[22] S.M.X.Fernando, D.M.Monro. Variable thresholding applied to angiography. The International Society for Optical Engineering, 1982, 375:152-156
[23] T. Pun. A new method for gray-level picture thresholding using the entropy of the histogram. Signal Processing, 1980, 2(3):223-237
[24] N. Otsu. A threshold selection method from gray-level histogram. IEEE Transactions on Systems, Man and Cybernetics, 1979, SMC-9:62-66
[25] J. S. Weszka and A. Rosenfeld. Threshold evaluation techniques. IEEE Transactions on Systems, Man and Cybernetics , 1978, SMC-8(8):622-629
[26] Thitiporn Chanwimaluang and Guoliang Fan. An efficient blood vessel detection algorithm for retinal images using local entropy thresholding. IEEE Transactions on Circuits and Systems, 2003,Vol5: 25-28
[28] Shechter G., Devemay F. Three-Dimensional Motion Tracking of Coronary Arteries in Biplane Cineangiograms. IEEE Transactions on Medical Imaging, 2000, 22(4): 493-503
[29] Keith M. Andress. Evidential Reconstruction of Vessel Trees from Rotational Angiograms. IEEE Conf. Image Processing, 1998, 3: 385-389.
[30] Lindeberg T. Edge Detection and Ridge Detection with Automatic Scale Selection. International Journal of Computer Vision, 1998, 30(2): 117-154
[31] Jang J. H., Hong K.S. A Pseudo-Distance Map for the Segmentation-FreeSkeletonization of Gray-Scale Images. Proceedings of the IEEE International Conference on Computer Vision, 2001, 2: 18-23
[32] Golland, P., Grimson, W.E.L. Fixed Topology Skeletons. Proc. IEEE Conf. Computer Vision and Pattern Recognition. 2000, 1: 10-17
[33] Maglaveras. N, Haris. K. Artery Skeleton Extraction Using Topographic and Connected Component Labeling. Computers in Cardiology, 2001, 28: 17-20.
[34] 刘潇潇,曹治国,李抱朴,等. 基于多尺度 Gabor 滤波的造影血管中轴线的自动提取.中国图象图形学报,2005, 10(12A):1542-1547
[35] Clausi D.A., Jernigan M.E. Designing Gabor filters for Optimal Texture Separability. Pattern Recognition, 2000, 33(11): 1835-1849.
[36] Wink. O, Niessen. W.J, Viergever, M.A. Multiscale vessel tracking. IEEE Transactions on Medical Imaging, Jan. 2004, 23(1):130-133
[37] S. Y James Chen, D. John. Carroll, J. C. Messenger. Quantitative analysis of reconstruction 3-D coronary arterial tree and intracoronary devices. IEEE Transaction on Medical Imaging, 2002, 21(7):724-740
[38] W. E. Hart, Michael Goldbaum, et al. Measurement and classification of retinal vascular tortuosity. International Journal of Medical Informatics, 1999, 53:239-252
[39] Geofrey Dougherty, Jozsef Varro. A quantitative index for the measurement of the tortuosity of blood vessels. Medical Engineering & Physics, 2000, 22(8):567-574
[40] Sarwal, A. P. Hhawan. Three dimensional reconstruction of coronary arteries from two views. Computer Methods and Programs in Biomedicine, 2001, 65(1):25-43
[41] 彭延军,石教英,体绘制技术在医学可视化中的新发展,中国图象图形学报,2002,7(12): 1237-1246
[42] 何小海,罗代升,陶青川等,三维虚拟物体表面重建的研究,中国图象图形学报,2002, 7(11): 1192-1197
[43] 张宗华,多视场深度图像的计算机辅助造型,[博士论文],天津:天津大学,2000
[44] W. L. Nowinski, A. Thirunavuukarasuu, R. Baimouratov. A combined anatomical-vascular 3D brain atlas. Scientific Assembly, 2003,11(10): 800.
[45] 石教英,蔡立文. 科学计算可视化算法与系统. 北京: 科学出版社, 1996. 96
[46] 廖琪梅,杭洽时, 钱宗才, 等. 三维医学图像重建的方法与应用. 现代电子技术, 1997 , 3(4) : 41 - 42
[47] 王延华,洪飞, 吴恩华. 基于VTK库的医学图像处理子系统设计和实现. 计算机工程与应用, 2003 , 8(10) : 207
[2] Keith M. Andress. Evidential Reconstruction of Vessel Trees from Rotational Angiograms. IEEE Transactions on Medical Imaging, 1998, 3: 385-389.
[3] R. T. Ritchings, A.C.F. Colchester. Detection of abnormalities on carotid angiograms. Pattern Recognition Letters, 1986, 4(5):367-374
[4] B. D. Thackray, A. C, Nelson. Semi-automatic segmentation of vascular network images using a rotating structuring element (ROSE) with mathematical morphology and dual feature thresholding. IEEE Transactions on Medical Imagine, 1993, 12(3):385- 392
[5] 1. Liu, Y Sun. Recursive tracking of vascular networks in angiograms based on the detection-deletion scheme. IEEE Transaction on medical imaging, 1993, 12(2):334- 341
[6] I. Liu, Y Sun. Recursive tracking of vascular trees in angiograms using a detection-deletion scheme. IEEE Workshop on Mathematical Methods in Biomedical Image Analysis , 1990, 4:169-170
[7] S. Aylward, S. Pizer, E. Bullit, and D. Eberl. Intensity ridge and widths for tubular object segmentation and description. IEEE Workshop on Mathematical Methods in Biomedical Image Analysis 1996, 2:131-138
[8] D. J. Stevenson, L. D. R. Smith, G. Robison. Working towards the automatic detection of blood vessels in x-ray angiograms. Pattern recognition letters, 1994, 16(1):295-301
[9] A. K. Klein, F. Lee, A. Amini. Quantitative coronary angiography withdeformable spline models. IEEE Transactions on medical imaging, 1997, 16(5): 468-482
[10] Hyun Wook Park, Todd Schoepfline, and Yongmin Kim. Active contour model with gradient directional information: directional snake. IEEE Transactions on Circuits and Systems for Video Technology, 2001, 11(2):252-256
[11] Amit Chakraboty, Lawrence H. Staib. Deformable Boundary Finding in Medical images by integrating gradient and region information. IEEE Transactions on Medical Imaging, Dec. 1996, 15(6):859-870
[12] S. A. Stansfiled. ANGY: A rule-Based expert System for automatic segmentation of coronary vessels from digital subtracted angiograms. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1986, 8(2):188-199
[13] C. Smets, G. Verbeeck, P Suetens. A knowledge-based system for the delineation of blood vessels on subtraction angiograms. Pattern Recognition Letters,1998, 8(2):113-121
[14] R. Nekovei, Y Sun. Back-propagation network and its configuration for blood vessel detection in angiograms. IEEE Transactions on neural networks. 1995, 6(1):64-72
[15] V Caselles, F. Cate, T. Coll, F. Dibos. A geometric model for active contours in image processing, Numerische Mathematik. 1993, 66(1):1-31
[16] R. Malladi, J. A. Sethian, and B.C. Vemuri. Shape modeling with front propagation: A level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(2):158-175
[17] Kirbas, C. and Quek, F. A Review of Vessel Extraction Techniques and Algorithms. ACM Computing Surveys (CSUR), June, 2004, 36(2): 81-121.
[18] Mehmet Sezgin, Bulent Sankur. Survey over image thresholding techniques and quantitative performance evaluation. Journal of Electronic Imaging ,2004, 13(1): 146–168
[19] Xiaoyi Jiang, Daniel Mojon. Adaptive Local Thresholding by Verification-Based Multithreshold Probing with Application to Vessel Detection in Retinal Images. IEEE Trans. Pattern analysis and Machine Intelligence, January 2003, 25(1): 131-137
[20] N. R. Pal, S. K. Pal. A review on image segmentation techniques. Pattern Recognition. 1993, 26(9): 1227-1249
[21] C.k.Chow, T.Kaneko. Automatic boundary of left ventricle from cineangiograms. Computers and Biomedical Research, 1972, 5(4):388-410
[22] S.M.X.Fernando, D.M.Monro. Variable thresholding applied to angiography. The International Society for Optical Engineering, 1982, 375:152-156
[23] T. Pun. A new method for gray-level picture thresholding using the entropy of the histogram. Signal Processing, 1980, 2(3):223-237
[24] N. Otsu. A threshold selection method from gray-level histogram. IEEE Transactions on Systems, Man and Cybernetics, 1979, SMC-9:62-66
[25] J. S. Weszka and A. Rosenfeld. Threshold evaluation techniques. IEEE Transactions on Systems, Man and Cybernetics , 1978, SMC-8(8):622-629
[26] Thitiporn Chanwimaluang and Guoliang Fan. An efficient blood vessel detection algorithm for retinal images using local entropy thresholding. IEEE Transactions on Circuits and Systems, 2003,Vol5: 25-28
[28] Shechter G., Devemay F. Three-Dimensional Motion Tracking of Coronary Arteries in Biplane Cineangiograms. IEEE Transactions on Medical Imaging, 2000, 22(4): 493-503
[29] Keith M. Andress. Evidential Reconstruction of Vessel Trees from Rotational Angiograms. IEEE Conf. Image Processing, 1998, 3: 385-389.
[30] Lindeberg T. Edge Detection and Ridge Detection with Automatic Scale Selection. International Journal of Computer Vision, 1998, 30(2): 117-154
[31] Jang J. H., Hong K.S. A Pseudo-Distance Map for the Segmentation-FreeSkeletonization of Gray-Scale Images. Proceedings of the IEEE International Conference on Computer Vision, 2001, 2: 18-23
[32] Golland, P., Grimson, W.E.L. Fixed Topology Skeletons. Proc. IEEE Conf. Computer Vision and Pattern Recognition. 2000, 1: 10-17
[33] Maglaveras. N, Haris. K. Artery Skeleton Extraction Using Topographic and Connected Component Labeling. Computers in Cardiology, 2001, 28: 17-20.
[34] 刘潇潇,曹治国,李抱朴,等. 基于多尺度 Gabor 滤波的造影血管中轴线的自动提取.中国图象图形学报,2005, 10(12A):1542-1547
[35] Clausi D.A., Jernigan M.E. Designing Gabor filters for Optimal Texture Separability. Pattern Recognition, 2000, 33(11): 1835-1849.
[36] Wink. O, Niessen. W.J, Viergever, M.A. Multiscale vessel tracking. IEEE Transactions on Medical Imaging, Jan. 2004, 23(1):130-133
[37] S. Y James Chen, D. John. Carroll, J. C. Messenger. Quantitative analysis of reconstruction 3-D coronary arterial tree and intracoronary devices. IEEE Transaction on Medical Imaging, 2002, 21(7):724-740
[38] W. E. Hart, Michael Goldbaum, et al. Measurement and classification of retinal vascular tortuosity. International Journal of Medical Informatics, 1999, 53:239-252
[39] Geofrey Dougherty, Jozsef Varro. A quantitative index for the measurement of the tortuosity of blood vessels. Medical Engineering & Physics, 2000, 22(8):567-574
[40] Sarwal, A. P. Hhawan. Three dimensional reconstruction of coronary arteries from two views. Computer Methods and Programs in Biomedicine, 2001, 65(1):25-43
[41] 彭延军,石教英,体绘制技术在医学可视化中的新发展,中国图象图形学报,2002,7(12): 1237-1246
[42] 何小海,罗代升,陶青川等,三维虚拟物体表面重建的研究,中国图象图形学报,2002, 7(11): 1192-1197
[43] 张宗华,多视场深度图像的计算机辅助造型,[博士论文],天津:天津大学,2000
[44] W. L. Nowinski, A. Thirunavuukarasuu, R. Baimouratov. A combined anatomical-vascular 3D brain atlas. Scientific Assembly, 2003,11(10): 800.
[45] 石教英,蔡立文. 科学计算可视化算法与系统. 北京: 科学出版社, 1996. 96
[46] 廖琪梅,杭洽时, 钱宗才, 等. 三维医学图像重建的方法与应用. 现代电子技术, 1997 , 3(4) : 41 - 42
[47] 王延华,洪飞, 吴恩华. 基于VTK库的医学图像处理子系统设计和实现. 计算机工程与应用, 2003 , 8(10) : 207