全自动肝脏门静脉分割算法的研究与实现
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摘要
在肝脏影像中进行血管分析对于肝脏的术前预案非常重要。外科医生必须对肝脏内部的血管系统有了深入的了解后,才能制定出合理的肝脏切除方案,从而避免手术中由于主血管破裂导致的不必要的大出血。
传统的肝脏血管分割方法主要基于区域增长或阈值分割算法,但是由于受到螺旋CT图像中噪音的影响,取得的分割效果并不理想。同时,传统方法还受到初始阶段不够自动化,缺乏有效的手段分离肝脏两大静脉系统等问题的困扰。
本文提出了一种鲁棒的,全自动的血管分割方法来提取肝脏影像中的门静脉系统,并将它与肝静脉系统分离。本文首先利用解剖学的先验知识来初步确定门静脉主干(PVT)的位置,然后通过直方图分析的方法来精确地提取门静脉主干切片区域,并将提取的区域作为分割需要的初始区域。随后,本文提出一种新颖的血管分割框架“基于血管切片区域的扩散”(Propagations of Vessel Regions on Slice,简称PVRS),并结合多尺度管状结构分析得到的vesselness度量来进行肝脏血管的分割。本文提出的分割框架是针对各向异性的体数据设计的,把三维空间的扩散划分成层间扩散和层内扩散两类,能有效地处理现有医院中螺旋CT产生的数据。最后,本文针对多种血管错连的情况提炼出四种剪枝规则,并通过图分析的方法将分割结果中由成像噪声产生的末梢、环路及错连的肝静脉血管从门静脉系统分离。
血管分割中的vesselness的计算比较耗时,本文通过自适应地确定每个体素的尺度范围,并结合PVRS框架中空间局部性的特点来进行优化。实验结果表明,优化后的计算速度比优化前计算速度快18倍左右。本文的血管分割算法还与基于灰度值的区域增长算法在临床的影像数据上进行了比较,取得了比区域增长算法更好的实验结果。
Hepatic vasculature analysis and visualization are very essential in preoperative surgery planning. Surgeons must have a deep understanding of intrahepatic vessel systems in order to determine a proper hepatic resection approach and avoid unnecessary massive hemorrhage caused by main vessel rupture in surgeries.
Traditional hepatic vessel segmentation methods were mainly based on region growing method and thresholding method. However, due to strong image noises in CT images, their segmentation results were unpromising. Besides, they also suffered the other two problems:lack of robust methods to automatically determine the initial parameters and to separate falsely connected portal system and hepatic venous system.
The aim of this paper is to provide a robust and fully automatic vessel segmentation method to extract the hepatic portal system and separate it from the hepatic venous system. Firstly, our method utilizes anatomical priors to roughly locate the portal vein trunk (PVT), and adopts histogram analysis to precisely extract it as the initial area, thereby automating the initialization of portal vein segmentation. Secondly, a novel vessel segmentation framework --- Propagations of Vessel Region on Slice (PVRS) is proposed and used with a vesselness measure derived from multiscale analysis of tubular structure to segment hepatic vasculature. This framework is anisotropic, which divides 3D propagation into inter-slice and intra-slice one and thus quite suitable to deal with anisotropic volume data such as CT data. Finally, graph analysis on the vascular skeleton is used to remove noisy end branches, loops and falsely connected hepatic veins from the portal system based on our pruning rules.
The computation of vesselness is quite time consuming. To accelerate its computation, we exploit the spatial locality of the framework and propose a way to adaptively determine the scale range of each voxel. The experimental results show that the optimized computation speed is roughly 18 times faster than the original one. In addition, we also compare our segmentation results with that of intensity-based region growing method and find our method achieves obviously better results.
传统的肝脏血管分割方法主要基于区域增长或阈值分割算法,但是由于受到螺旋CT图像中噪音的影响,取得的分割效果并不理想。同时,传统方法还受到初始阶段不够自动化,缺乏有效的手段分离肝脏两大静脉系统等问题的困扰。
本文提出了一种鲁棒的,全自动的血管分割方法来提取肝脏影像中的门静脉系统,并将它与肝静脉系统分离。本文首先利用解剖学的先验知识来初步确定门静脉主干(PVT)的位置,然后通过直方图分析的方法来精确地提取门静脉主干切片区域,并将提取的区域作为分割需要的初始区域。随后,本文提出一种新颖的血管分割框架“基于血管切片区域的扩散”(Propagations of Vessel Regions on Slice,简称PVRS),并结合多尺度管状结构分析得到的vesselness度量来进行肝脏血管的分割。本文提出的分割框架是针对各向异性的体数据设计的,把三维空间的扩散划分成层间扩散和层内扩散两类,能有效地处理现有医院中螺旋CT产生的数据。最后,本文针对多种血管错连的情况提炼出四种剪枝规则,并通过图分析的方法将分割结果中由成像噪声产生的末梢、环路及错连的肝静脉血管从门静脉系统分离。
血管分割中的vesselness的计算比较耗时,本文通过自适应地确定每个体素的尺度范围,并结合PVRS框架中空间局部性的特点来进行优化。实验结果表明,优化后的计算速度比优化前计算速度快18倍左右。本文的血管分割算法还与基于灰度值的区域增长算法在临床的影像数据上进行了比较,取得了比区域增长算法更好的实验结果。
Hepatic vasculature analysis and visualization are very essential in preoperative surgery planning. Surgeons must have a deep understanding of intrahepatic vessel systems in order to determine a proper hepatic resection approach and avoid unnecessary massive hemorrhage caused by main vessel rupture in surgeries.
Traditional hepatic vessel segmentation methods were mainly based on region growing method and thresholding method. However, due to strong image noises in CT images, their segmentation results were unpromising. Besides, they also suffered the other two problems:lack of robust methods to automatically determine the initial parameters and to separate falsely connected portal system and hepatic venous system.
The aim of this paper is to provide a robust and fully automatic vessel segmentation method to extract the hepatic portal system and separate it from the hepatic venous system. Firstly, our method utilizes anatomical priors to roughly locate the portal vein trunk (PVT), and adopts histogram analysis to precisely extract it as the initial area, thereby automating the initialization of portal vein segmentation. Secondly, a novel vessel segmentation framework --- Propagations of Vessel Region on Slice (PVRS) is proposed and used with a vesselness measure derived from multiscale analysis of tubular structure to segment hepatic vasculature. This framework is anisotropic, which divides 3D propagation into inter-slice and intra-slice one and thus quite suitable to deal with anisotropic volume data such as CT data. Finally, graph analysis on the vascular skeleton is used to remove noisy end branches, loops and falsely connected hepatic veins from the portal system based on our pruning rules.
The computation of vesselness is quite time consuming. To accelerate its computation, we exploit the spatial locality of the framework and propose a way to adaptively determine the scale range of each voxel. The experimental results show that the optimized computation speed is roughly 18 times faster than the original one. In addition, we also compare our segmentation results with that of intensity-based region growing method and find our method achieves obviously better results.
引文
1 PRS全称为Propagating Region Structure,即扩散区域结构,它包括一个扩散方向(向上层或下层扩散)和一个需要扩散的血管区域。
1 图中由虚线包围的区域为层间扩散得到的种子区域,而由实线包围的则是层内扩散在种子区域的基础上优化得到的血管区域。
1 图5-5中红色线段为门静脉血管,蓝色线段为肝静脉血管,绿色线段为去除的噪音分枝。
1 蓝色区域为体素16和体素19共享的邻域,当体素16的vesselness计算完后,体素19的vesselness所需的2/3卷积强度值无需被重复计算,大大减少了vesselness计算时间。
[1]Couinaud, C. Le Foie:Etudes anatomiques et chirurgicales Pairs, Masson,1957
[2]Fasel J, Gailloud P, Terrier F, Mentha G, Sprumont P. Segmental anatomy of the liver:a review and proposal for an international working nomenclature. Eur Radiol, 1996,6(6):834-837
[3]Fasel J, Selle D, Evertsz C, Terrier F, Peitgen H, Gailloud P. Segmental anatomy of the liver:poor correlation with CT. Radiology,1998(206):151-156
[4]Goldsmith N, Woodburne R. The surgical anatomy pertaining to liver resections. Surg Gynecol Obstet,1957(105):310-318
[5]C. C. Meltzer, J. P. Leal, H. S. Mayberg, H. J. Wagner, J. J. Frost. Correction of PET data for partial-volume effects in human cerebral cortex by MR imaging. J.Comput. Assist. Tomogr.,1990,14(4):561-570
[6]H. W Muller-Gartner, J. M. Links, J. L. Prince, R. N. Bryan, E. McVeigh, J. P. Leal, C. Davatzikos, J. J. Frost. Measurement of radiotracer concentration in brain gray matter using positron emission tomography:MPI-based correction for partial volume effects. Journal of Cerebral Blood Flow & Metabolism,1992,12(4):571-583
[7]Olivier G. Rousset, Yilong Ma and Alan C. Evans. Correction for Partial Volume Effects in PET:Principle and Validation. The Journal of Nuclear Medicine,1998, 39(5):904-911
[8]Carolyn Cidis Meltzer, Paul E. Kinahan, Phil J. Greer, Thomas E. Nichols, Claude Comtat, Michael N. Cantwell, Michael P. Lin and Julie C. Price. Comparative Evaluation of MR-Based Partial-Volume Correction Schemes for PET. The Journal of Nuclear Medicine,1999,40(12):2053-2065
[9]Soler, L., H. Delingette, et al. Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery. Medical Imaging 2000: Image Processing, Pts 1 and 2,2000:246-255
[10]Selle, D., B. Preim, et al. Analysis of vasculature for liver surgical planning. IEEE TRANSACTIONS ON MEDICAL IMAGING,2002,21(11):1344-1357
[11]Beichel, R., T. Pock, et al. Liver segment approximation in CT data for surgical resection planning. Medical Imaging 2004:Image Processing, Pts 1-3,2004(5370): 1435-1446
[12]Eidheim, O. C., L. Aurdal, et al. Segmentation of liver vessels as seen in MR and CT images. Computer Assisted Radiology and Surgery,2004
[13]Frericks, B. B., F. C. Caldarone, et al..3D CT modeling of hepatic vessel architecture and volume calculation in living donated liver transplantation. European Radiology,2004,14(2):326-333
[14]Fetita, C., O. Lucidarme, et al. CT hepatic venography:3D vascular segmentation for preoperative evaluation. Medical Image Computing and Computer-Assisted Intervention,2005
[15]Freiman, M., O. Eliassaf, et al. A bayesian approach for liver analysis:algorithm and validation study. Medical Image Computing and Computer-Assisted Intervention 2008,11(1):85-92
[16]Homann, H., G. Vesom, et al. Vasculature segmentation of CT liver images using graph cuts and graph-based analysis. IEEE International Symposium on Biomedical Imaging:From Nano to Macro,2008(1-4):53-56
[17]Huang, S., B. Wang, et al. The use of a projection method to simplify portal and hepatic vein segmentation in liver anatomy. computer methods and programs in biomedicine,2008,92(3):274-278
[18]Shang, Q. G., L. Clements, et al. Adaptive directional region growing segmentation of the hepatic vasculature. Medical Imaging 2008:Image Processing, Pts 1-3,2008(6914)
[19]Sboarina, A., R. I. Foroni, et al. Software for hepatic vessel classification: feasibility study for virtual surgery. Int J Comput Assist Radiol Surg,2009,5(1): 39-48
[20]Cormen, T. H., C. Stein, et al. Introduction to Algorithms, McGraw-Hill Higher Education,2001
[21]Osher, S., Sethian, J. A. Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys.1988(79): 12-49
[22]Osher, Stanley J., Fedkiw, Ronald P. Level Set Methods and Dynamic Implicit Surfaces. Springer-Verlag,2002
[23]Sethian, James A. Level Set Methods and Fast Marching Methods:Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Cambridge University Press,1999
[24]Enright, D., Fedkiw, R. P., Ferziger, J. H., Mitchell, I. A hybrid particle level set method for improved interface capturing. J. Comput. Phys,2002(183):83-116
[25]Gonzalez, R. C. and R. E. Woods. Digital Image Processing, Addison-Wesley Longman Publishing Co., Inc,1992
[26]Netter, F. Atlas of Human Anatomy. Rittenhouse Book Distributors Inc,1997
[27]Song, Y., A. Bulpitt, et al. Liver Segmentation Using Automatically Defined Patient Specific B-Spline Surface Models. Medical Image Computing and Computer-Assisted Intervention-MICCAI,2009:43-50
[28]Campadelli, P., Casiraghi, E. Liver Segmentation from CT Scans:A Survey. WILF,2007(4578):520-528
[29]Florin, C., Paragios, N., Funka-Lea, G., Williams, J. Liver Segmentation Using Sparse 3D Prior Models with Optimal Data Support. IPMI,2007(4584):38-49
[30]Ling, H., Zhou, S.K., Zheng, Y., et al. Hierarchical, Learning-based Automatic Liver Segmentation. IEEE conf. on Computer Vision and Pattern Recognition, 2008:1-8
[31]Heimann, T., Meinzer, H., Wolf, I. A Statistical Deformable model for the Segmentation of Liver CT Volumes. MICCAI workshop on 3D Segmentation in the Clinic (SLIVER 2007),2007:161-166
[32]Lamecker, H., Lange, T., Seebass, M. A Statistical Shape Model for the Liver. MICCAI,2002(2489):421-427
[33]Heimann, T., van Ginneken, B., Styner, M., et al. Comparison and Evaluation of Methods for Liver Segmentation from CT datasets. J. IEEE Trans. on Medical Imaging,2009
[34]Kainmueller, D., Lange, T., Lamecker, H. Shape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model. MICCAI workshop on 3D Segmentation in the Clinic,2007:109-116
[35]Rusko, L., Bekes, G., Nemeth, G., Fidrich, M. Fully Automatic Liver Segmentation for Contrast-enhanced CT images. MICCAI workshop on 3D Segmentation in the Clinic,2007:143-150
[36]Lee, J., Kim, N., Lee, H., et al. Efficient Liver Segmentation Exploiting Level-set Speed Images with 2.5D Shape Propagation. MICCAI workshop on 3D Segmentation in the Clinic,2007:189-196
[37]Isaac Bankman. Handbook of Medical Image Processing and Analysis, Second Edition. Academic Press,2008
[38]Xu, L. and S. Luo. A novel method for blood vessel detection from retinal images. BioMedical Engineering OnLine,2010,9(1):14
[39]. Y. Sato, S. Nakajima, H. Atsumi, T. Koller, G. Gerig, S. Yoshida, and R. Kikinis.3D Multi-Scale Line Filter for Segmentation and Visualization of Curvilinear Structures in Medical Images, in First Joint Conference of CVRMed Ⅱ and MRCAS Ⅲ (CVRMed-MRCAS'97), Lecture Notes in Computer Science,1997(1205): 213-222
[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 First Joint Conference of CVRMed Ⅱ and MRCAS Ⅲ (CVRMed-MRCAS'97), Lecture Notes in Computer Science, 1997(1205):233-242
[41]Nancy M. Salem, Asoke K. Nandi. Unsupervised Segmentation of Retinal Blood Vessels Using a Single Parameter Vesselness Measure. Computer Vision, Graphics & Image Processing, Indian Conference on,2008:528-534
[42]Pechin Lo, Jon Sporring, Haseem Ashraf, Jesper J.H. Pedersen, Marleen de Bruijne. Vessel-guided airway tree segmentation:A voxel classification approach. Medical Image Analysis,2010,14(4):527-538
[43]Hidenori Shikata, Geoffrey McLennan, Eric A. Hoffman, and Milan Sonka. Segmentation of Pulmonary Vascular Trees from Thoracic 3D CT Images. International Journal of Biomedical Imaging,2009
[44]Aylward SR, Bullitt E. Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction. IEEE Trans Med Imaging,2002(21): 61-75
[45]Wink O, Niessen WJ, Viergever MA. Multiscale vessel tracking. IEEE Trans Med Imaging,2004(23):130-133
[46]Lee, T.-C., R. L. Kashyap, et al. Building skeleton models via 3-D medial surface/axis thinning algorithms. CVGIP:Graph. Models Image Process,1994,56(6): 462-478
[47]Palagyi, K. and A. Kuba. A parallel 12-subiteration 3D thinning algorithm to extract medial lines. Computer Analysis of Images and Patterns,1997:400-407
[48]Palagyi, K. and A. Kuba. A 3D 6-subiteration thinning algorithm for extracting medial lines. Pattern Recognition Letters,1998,19(7):613-627
[49]Palagyi, K., E. Sorantin, et al. A Sequential 3D Thinning Algorithm and Its Medical Applications. Proceedings of the 17th International Conference on Information Processing in Medical Imaging,2001
[50]Saha, P.K., Chaudhuri, B.B. Detection of 3-D simple points for topology preserving transformations with application to thinning. IEEE Transactions on Pattern Analysis and Machine Intelligence,1994(16):1028-1032
[51]罗述谦,周果宏.医学图像处理与分析.科学出版社,2003.
[52]晏福旗,程明,王博亮.三维肝脏CT图像中的血管分割.中国数字医学,2010.
[53]彭丰平,鲍苏苏.基于分割的肝癌三维可视化研究与实现.计算机与数字工程,2008.
1 图中由虚线包围的区域为层间扩散得到的种子区域,而由实线包围的则是层内扩散在种子区域的基础上优化得到的血管区域。
1 图5-5中红色线段为门静脉血管,蓝色线段为肝静脉血管,绿色线段为去除的噪音分枝。
1 蓝色区域为体素16和体素19共享的邻域,当体素16的vesselness计算完后,体素19的vesselness所需的2/3卷积强度值无需被重复计算,大大减少了vesselness计算时间。
[1]Couinaud, C. Le Foie:Etudes anatomiques et chirurgicales Pairs, Masson,1957
[2]Fasel J, Gailloud P, Terrier F, Mentha G, Sprumont P. Segmental anatomy of the liver:a review and proposal for an international working nomenclature. Eur Radiol, 1996,6(6):834-837
[3]Fasel J, Selle D, Evertsz C, Terrier F, Peitgen H, Gailloud P. Segmental anatomy of the liver:poor correlation with CT. Radiology,1998(206):151-156
[4]Goldsmith N, Woodburne R. The surgical anatomy pertaining to liver resections. Surg Gynecol Obstet,1957(105):310-318
[5]C. C. Meltzer, J. P. Leal, H. S. Mayberg, H. J. Wagner, J. J. Frost. Correction of PET data for partial-volume effects in human cerebral cortex by MR imaging. J.Comput. Assist. Tomogr.,1990,14(4):561-570
[6]H. W Muller-Gartner, J. M. Links, J. L. Prince, R. N. Bryan, E. McVeigh, J. P. Leal, C. Davatzikos, J. J. Frost. Measurement of radiotracer concentration in brain gray matter using positron emission tomography:MPI-based correction for partial volume effects. Journal of Cerebral Blood Flow & Metabolism,1992,12(4):571-583
[7]Olivier G. Rousset, Yilong Ma and Alan C. Evans. Correction for Partial Volume Effects in PET:Principle and Validation. The Journal of Nuclear Medicine,1998, 39(5):904-911
[8]Carolyn Cidis Meltzer, Paul E. Kinahan, Phil J. Greer, Thomas E. Nichols, Claude Comtat, Michael N. Cantwell, Michael P. Lin and Julie C. Price. Comparative Evaluation of MR-Based Partial-Volume Correction Schemes for PET. The Journal of Nuclear Medicine,1999,40(12):2053-2065
[9]Soler, L., H. Delingette, et al. Fully automatic anatomical, pathological, and functional segmentation from CT scans for hepatic surgery. Medical Imaging 2000: Image Processing, Pts 1 and 2,2000:246-255
[10]Selle, D., B. Preim, et al. Analysis of vasculature for liver surgical planning. IEEE TRANSACTIONS ON MEDICAL IMAGING,2002,21(11):1344-1357
[11]Beichel, R., T. Pock, et al. Liver segment approximation in CT data for surgical resection planning. Medical Imaging 2004:Image Processing, Pts 1-3,2004(5370): 1435-1446
[12]Eidheim, O. C., L. Aurdal, et al. Segmentation of liver vessels as seen in MR and CT images. Computer Assisted Radiology and Surgery,2004
[13]Frericks, B. B., F. C. Caldarone, et al..3D CT modeling of hepatic vessel architecture and volume calculation in living donated liver transplantation. European Radiology,2004,14(2):326-333
[14]Fetita, C., O. Lucidarme, et al. CT hepatic venography:3D vascular segmentation for preoperative evaluation. Medical Image Computing and Computer-Assisted Intervention,2005
[15]Freiman, M., O. Eliassaf, et al. A bayesian approach for liver analysis:algorithm and validation study. Medical Image Computing and Computer-Assisted Intervention 2008,11(1):85-92
[16]Homann, H., G. Vesom, et al. Vasculature segmentation of CT liver images using graph cuts and graph-based analysis. IEEE International Symposium on Biomedical Imaging:From Nano to Macro,2008(1-4):53-56
[17]Huang, S., B. Wang, et al. The use of a projection method to simplify portal and hepatic vein segmentation in liver anatomy. computer methods and programs in biomedicine,2008,92(3):274-278
[18]Shang, Q. G., L. Clements, et al. Adaptive directional region growing segmentation of the hepatic vasculature. Medical Imaging 2008:Image Processing, Pts 1-3,2008(6914)
[19]Sboarina, A., R. I. Foroni, et al. Software for hepatic vessel classification: feasibility study for virtual surgery. Int J Comput Assist Radiol Surg,2009,5(1): 39-48
[20]Cormen, T. H., C. Stein, et al. Introduction to Algorithms, McGraw-Hill Higher Education,2001
[21]Osher, S., Sethian, J. A. Fronts propagating with curvature-dependent speed: Algorithms based on Hamilton-Jacobi formulations. J. Comput. Phys.1988(79): 12-49
[22]Osher, Stanley J., Fedkiw, Ronald P. Level Set Methods and Dynamic Implicit Surfaces. Springer-Verlag,2002
[23]Sethian, James A. Level Set Methods and Fast Marching Methods:Evolving Interfaces in Computational Geometry, Fluid Mechanics, Computer Vision, and Materials Science. Cambridge University Press,1999
[24]Enright, D., Fedkiw, R. P., Ferziger, J. H., Mitchell, I. A hybrid particle level set method for improved interface capturing. J. Comput. Phys,2002(183):83-116
[25]Gonzalez, R. C. and R. E. Woods. Digital Image Processing, Addison-Wesley Longman Publishing Co., Inc,1992
[26]Netter, F. Atlas of Human Anatomy. Rittenhouse Book Distributors Inc,1997
[27]Song, Y., A. Bulpitt, et al. Liver Segmentation Using Automatically Defined Patient Specific B-Spline Surface Models. Medical Image Computing and Computer-Assisted Intervention-MICCAI,2009:43-50
[28]Campadelli, P., Casiraghi, E. Liver Segmentation from CT Scans:A Survey. WILF,2007(4578):520-528
[29]Florin, C., Paragios, N., Funka-Lea, G., Williams, J. Liver Segmentation Using Sparse 3D Prior Models with Optimal Data Support. IPMI,2007(4584):38-49
[30]Ling, H., Zhou, S.K., Zheng, Y., et al. Hierarchical, Learning-based Automatic Liver Segmentation. IEEE conf. on Computer Vision and Pattern Recognition, 2008:1-8
[31]Heimann, T., Meinzer, H., Wolf, I. A Statistical Deformable model for the Segmentation of Liver CT Volumes. MICCAI workshop on 3D Segmentation in the Clinic (SLIVER 2007),2007:161-166
[32]Lamecker, H., Lange, T., Seebass, M. A Statistical Shape Model for the Liver. MICCAI,2002(2489):421-427
[33]Heimann, T., van Ginneken, B., Styner, M., et al. Comparison and Evaluation of Methods for Liver Segmentation from CT datasets. J. IEEE Trans. on Medical Imaging,2009
[34]Kainmueller, D., Lange, T., Lamecker, H. Shape Constrained Automatic Segmentation of the Liver based on a Heuristic Intensity Model. MICCAI workshop on 3D Segmentation in the Clinic,2007:109-116
[35]Rusko, L., Bekes, G., Nemeth, G., Fidrich, M. Fully Automatic Liver Segmentation for Contrast-enhanced CT images. MICCAI workshop on 3D Segmentation in the Clinic,2007:143-150
[36]Lee, J., Kim, N., Lee, H., et al. Efficient Liver Segmentation Exploiting Level-set Speed Images with 2.5D Shape Propagation. MICCAI workshop on 3D Segmentation in the Clinic,2007:189-196
[37]Isaac Bankman. Handbook of Medical Image Processing and Analysis, Second Edition. Academic Press,2008
[38]Xu, L. and S. Luo. A novel method for blood vessel detection from retinal images. BioMedical Engineering OnLine,2010,9(1):14
[39]. Y. Sato, S. Nakajima, H. Atsumi, T. Koller, G. Gerig, S. Yoshida, and R. Kikinis.3D Multi-Scale Line Filter for Segmentation and Visualization of Curvilinear Structures in Medical Images, in First Joint Conference of CVRMed Ⅱ and MRCAS Ⅲ (CVRMed-MRCAS'97), Lecture Notes in Computer Science,1997(1205): 213-222
[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 First Joint Conference of CVRMed Ⅱ and MRCAS Ⅲ (CVRMed-MRCAS'97), Lecture Notes in Computer Science, 1997(1205):233-242
[41]Nancy M. Salem, Asoke K. Nandi. Unsupervised Segmentation of Retinal Blood Vessels Using a Single Parameter Vesselness Measure. Computer Vision, Graphics & Image Processing, Indian Conference on,2008:528-534
[42]Pechin Lo, Jon Sporring, Haseem Ashraf, Jesper J.H. Pedersen, Marleen de Bruijne. Vessel-guided airway tree segmentation:A voxel classification approach. Medical Image Analysis,2010,14(4):527-538
[43]Hidenori Shikata, Geoffrey McLennan, Eric A. Hoffman, and Milan Sonka. Segmentation of Pulmonary Vascular Trees from Thoracic 3D CT Images. International Journal of Biomedical Imaging,2009
[44]Aylward SR, Bullitt E. Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction. IEEE Trans Med Imaging,2002(21): 61-75
[45]Wink O, Niessen WJ, Viergever MA. Multiscale vessel tracking. IEEE Trans Med Imaging,2004(23):130-133
[46]Lee, T.-C., R. L. Kashyap, et al. Building skeleton models via 3-D medial surface/axis thinning algorithms. CVGIP:Graph. Models Image Process,1994,56(6): 462-478
[47]Palagyi, K. and A. Kuba. A parallel 12-subiteration 3D thinning algorithm to extract medial lines. Computer Analysis of Images and Patterns,1997:400-407
[48]Palagyi, K. and A. Kuba. A 3D 6-subiteration thinning algorithm for extracting medial lines. Pattern Recognition Letters,1998,19(7):613-627
[49]Palagyi, K., E. Sorantin, et al. A Sequential 3D Thinning Algorithm and Its Medical Applications. Proceedings of the 17th International Conference on Information Processing in Medical Imaging,2001
[50]Saha, P.K., Chaudhuri, B.B. Detection of 3-D simple points for topology preserving transformations with application to thinning. IEEE Transactions on Pattern Analysis and Machine Intelligence,1994(16):1028-1032
[51]罗述谦,周果宏.医学图像处理与分析.科学出版社,2003.
[52]晏福旗,程明,王博亮.三维肝脏CT图像中的血管分割.中国数字医学,2010.
[53]彭丰平,鲍苏苏.基于分割的肝癌三维可视化研究与实现.计算机与数字工程,2008.