冠状动脉造影图像的分割方法研究
|
摘要
冠状动脉造影图像的分割是图像分割技术在医学领域的重要应用,冠脉血管的精确提取可以辅助医生诊断心血管疾病并确定治疗方案,同时也是血管三维重建的基础,在临床医疗中发挥着重要作用。本文对冠脉造影图像的分割方法进行了研究。
针对基于形态学方法对小血管分割效果不理想、基于高斯滤波法提取小血管的同时保留噪声的缺点,提出了一种基于融合的冠状动脉造影图像分割方法。该方法可以有效地分割提取血管主干和小血管,同时将噪声碎片有效地去除了。
针对梯度算子对噪声敏感的问题,构造了局部熵信息测度作为过渡区提取的特征参数,提出基于局部熵信息测度的阈值分割方法和融合分割方法。阈值方法能有效地分割提取主血管,但对小血管的分割效果不理想。融合方法在小血管的分割提取及连续性方面有了较大改善。
针对局部熵的运算量大、运算时间长的问题,构造参数局部复杂度信息测度,并提出两种基于局部复杂度信息测度的冠脉造影图像分割方法。仿真实验验证了两种算法能有效地分割提取冠脉血管,同时减少运算量,节省运算时间。
针对现有算法分割提取的冠脉血管连续性差的问题,将图论知识引入造影图像分割,构造度信息邻域非一致性测度,提出两种基于度信息邻域非一致性测度的冠脉造影图像分割方法。两种方法不同程度地提高了小血管的分割质量及小血管的连续性。
The heart cerebrovascular disease is the main threats to the human health currently. Now there are lots of diagnostic approaches about cardiovascular disease, angiography is the most reliable method of all. For angiography, the contrast medium is injected to the blood stream, then the heart and great vessels are developed to X-rays so that contraction and relaxation function of the heart can be clearly observed. The doctor can observe whether the coronary arteries are narrow,whether the blood clot exists by angiography images. Then the doctor can decide whether the blood circulatory system and the heart are normal. So, the angiography images can provide more and more accuracy information than other diagnostic methods. The coronary angiogram segmentation is an important application about image segmentation technologies in the medical field. After the image being segmented, the structure of vessels can be widely applied, such as assisting the doctor to diagnosis, quantitative analysis on vascular tissue, accurate positioning the narrow part of vessels and directing the doctor to operate and so on. Considering the importance of coronary vessels segmentation, in the paper the feature of coronary angiogram images is analyzed and the segmentation methods are discussed. The main works and innovations include:
1. The research on the segmentation methods of coronary angiogram images based on fusion.
The two popular methods about coronary angiogram segmentation are coronary arteries segmentation based on morphologic method and coronary arteries segmentation based on Gaussian filter. The morphologic top-hat method can partly enhance the grey of vessels. However, the grey enhancement of tiny blood vessels is not obvious due to the similarity between the grey of tiny blood vessels and the grey of background, so that the tiny blood vessels will be removed as background. The grey feature of vessels is considered in the segmentation methods based on Gaussian filter which can extract the major blood vessels and tiny blood vessels efficiently. However, it also extracts noises which are similar to the size of tiny blood vessels. In the paper, the results of two segmentation methods are analyzed by the region connectivity and the segmentation method of coronary angiogram images based on fusion is proposed. At first, the top-hat method and Gaussian filter method are used to enhance the same coronary angiogram, then two enhanced images can be obtained. After that, the method of optimal entropy is used to segment blood vessels and two images containing coronary arteries can be derived. At last, two images are fused and blood vessels are extracted. Results show the method can extract major blood vessels and tiny blood vessels efficiently, and the noises can be removed at the same time.
2. The research on the segmentation methods of coronary angiogram images based on local entropy information measure.
The extraction and segmentation methods based on transition region are a threshold segmentation reported currently. The traditional exaction method of transition region is an indirect extraction method by clip transformation of grayscale and the average of gradient. Gradient is the feature parameter for extracting transition region, which is so sensitive to noises that transition region sometimes can not be extracted. In the paper, the characteristics of coronary angiogram transition region is analyzed. The key to the transition region extraction is the selection of the feature parameters. According to the characteristic of transition region, the local entropy information measure is constructed as feature parameters for extracting transition region and the two segmentation methods of coronary angiogram images based on local entropy information measure are proposed. The first approach based on threshold segmentation of transition region extraction uses local entropy information measure to extract the transition region of angiogram and determines segmentation threshold by histogram of transition region. This method can extract the trunk of blood vessels properly without good result for extracting the tiny blood vessels. So, the fusion method based on transition region is proposed. For this method, the major blood vessels and the transition region of images are extracted firstly, then using the difference of blood vessels and noise fragments, blood vessels are extracted by analyzing the region connectivity. The major blood vessels and tiny blood vessels can be extracted efficiently. The connectivity among tiny blood vessels is better than other methods mentioned in the paper..
3. The research on the segmentation methods of coronary angiogram images based on local complexity information measure.
Local entropy is an important parameter of local entropy information measure which can denote the grayscale changing frequency of image local neighborhood. Computing local entropy is a complicated and difficult process, local complexity is a statistics about the grey level change in image local neighborhood. Local entropy can be taken place by local complexity as the parameter of image grey change. In the paper, the characteristics of local complexity is analyzed and the viewpoints of informatics are combined to construct the new feature parameters for extracting transition region. Two segmentation methods of coronary arteries are proposed by using local complexity information measure. Threshold method can extract the trunk of blood vessels without good extraction effection for tiny blood vessels. The fusion method based on transition region extraction can improve the extraction of tiny blood vessels and remove the background noises. Comparing the method based on local entropy information measure to the method based on local complexity information measure, the results show the methods based on local complexity information measure can reduce computational complexity and save computational time.
4. The research on the segmentation methods of coronary angiogram images based on neighborhood unhomogeneity of degree information.
The method of image segmentation based on graph theory is a new research hotspot in the field of image segmentation, currently. Graph theory is a branch of mathematics, its research object is graph and the graph is regard as a figure constructed by some points and some lines which connect two points. An image can be mapped into a weighted undirected graph, the pixels in the image can be looked as the nodes of graph and the edges can be formed between every pair of nodes. The degree of nodes is an important parameter of undirected graph which is decided by the weight of edges linking to nodes. Considering of the poor connectivity of coronary arteries extracted by existing segmentation algorithm, graph theory is introduced to angiogram segmentation. According to the characteristics of degree information, another measurement parameter of image transition region extraction is constructed which is the neighborhood unhomogeneity of degree information. In the paper, two segmentation methods of coronary angiogram images based on degree information neighborhood unhomogeneity are proposed after exacting the transition region. The two methods improve the quality of tiny vessels extraction and the connectivity of tiny blood vessels on different degree.
针对基于形态学方法对小血管分割效果不理想、基于高斯滤波法提取小血管的同时保留噪声的缺点,提出了一种基于融合的冠状动脉造影图像分割方法。该方法可以有效地分割提取血管主干和小血管,同时将噪声碎片有效地去除了。
针对梯度算子对噪声敏感的问题,构造了局部熵信息测度作为过渡区提取的特征参数,提出基于局部熵信息测度的阈值分割方法和融合分割方法。阈值方法能有效地分割提取主血管,但对小血管的分割效果不理想。融合方法在小血管的分割提取及连续性方面有了较大改善。
针对局部熵的运算量大、运算时间长的问题,构造参数局部复杂度信息测度,并提出两种基于局部复杂度信息测度的冠脉造影图像分割方法。仿真实验验证了两种算法能有效地分割提取冠脉血管,同时减少运算量,节省运算时间。
针对现有算法分割提取的冠脉血管连续性差的问题,将图论知识引入造影图像分割,构造度信息邻域非一致性测度,提出两种基于度信息邻域非一致性测度的冠脉造影图像分割方法。两种方法不同程度地提高了小血管的分割质量及小血管的连续性。
The heart cerebrovascular disease is the main threats to the human health currently. Now there are lots of diagnostic approaches about cardiovascular disease, angiography is the most reliable method of all. For angiography, the contrast medium is injected to the blood stream, then the heart and great vessels are developed to X-rays so that contraction and relaxation function of the heart can be clearly observed. The doctor can observe whether the coronary arteries are narrow,whether the blood clot exists by angiography images. Then the doctor can decide whether the blood circulatory system and the heart are normal. So, the angiography images can provide more and more accuracy information than other diagnostic methods. The coronary angiogram segmentation is an important application about image segmentation technologies in the medical field. After the image being segmented, the structure of vessels can be widely applied, such as assisting the doctor to diagnosis, quantitative analysis on vascular tissue, accurate positioning the narrow part of vessels and directing the doctor to operate and so on. Considering the importance of coronary vessels segmentation, in the paper the feature of coronary angiogram images is analyzed and the segmentation methods are discussed. The main works and innovations include:
1. The research on the segmentation methods of coronary angiogram images based on fusion.
The two popular methods about coronary angiogram segmentation are coronary arteries segmentation based on morphologic method and coronary arteries segmentation based on Gaussian filter. The morphologic top-hat method can partly enhance the grey of vessels. However, the grey enhancement of tiny blood vessels is not obvious due to the similarity between the grey of tiny blood vessels and the grey of background, so that the tiny blood vessels will be removed as background. The grey feature of vessels is considered in the segmentation methods based on Gaussian filter which can extract the major blood vessels and tiny blood vessels efficiently. However, it also extracts noises which are similar to the size of tiny blood vessels. In the paper, the results of two segmentation methods are analyzed by the region connectivity and the segmentation method of coronary angiogram images based on fusion is proposed. At first, the top-hat method and Gaussian filter method are used to enhance the same coronary angiogram, then two enhanced images can be obtained. After that, the method of optimal entropy is used to segment blood vessels and two images containing coronary arteries can be derived. At last, two images are fused and blood vessels are extracted. Results show the method can extract major blood vessels and tiny blood vessels efficiently, and the noises can be removed at the same time.
2. The research on the segmentation methods of coronary angiogram images based on local entropy information measure.
The extraction and segmentation methods based on transition region are a threshold segmentation reported currently. The traditional exaction method of transition region is an indirect extraction method by clip transformation of grayscale and the average of gradient. Gradient is the feature parameter for extracting transition region, which is so sensitive to noises that transition region sometimes can not be extracted. In the paper, the characteristics of coronary angiogram transition region is analyzed. The key to the transition region extraction is the selection of the feature parameters. According to the characteristic of transition region, the local entropy information measure is constructed as feature parameters for extracting transition region and the two segmentation methods of coronary angiogram images based on local entropy information measure are proposed. The first approach based on threshold segmentation of transition region extraction uses local entropy information measure to extract the transition region of angiogram and determines segmentation threshold by histogram of transition region. This method can extract the trunk of blood vessels properly without good result for extracting the tiny blood vessels. So, the fusion method based on transition region is proposed. For this method, the major blood vessels and the transition region of images are extracted firstly, then using the difference of blood vessels and noise fragments, blood vessels are extracted by analyzing the region connectivity. The major blood vessels and tiny blood vessels can be extracted efficiently. The connectivity among tiny blood vessels is better than other methods mentioned in the paper..
3. The research on the segmentation methods of coronary angiogram images based on local complexity information measure.
Local entropy is an important parameter of local entropy information measure which can denote the grayscale changing frequency of image local neighborhood. Computing local entropy is a complicated and difficult process, local complexity is a statistics about the grey level change in image local neighborhood. Local entropy can be taken place by local complexity as the parameter of image grey change. In the paper, the characteristics of local complexity is analyzed and the viewpoints of informatics are combined to construct the new feature parameters for extracting transition region. Two segmentation methods of coronary arteries are proposed by using local complexity information measure. Threshold method can extract the trunk of blood vessels without good extraction effection for tiny blood vessels. The fusion method based on transition region extraction can improve the extraction of tiny blood vessels and remove the background noises. Comparing the method based on local entropy information measure to the method based on local complexity information measure, the results show the methods based on local complexity information measure can reduce computational complexity and save computational time.
4. The research on the segmentation methods of coronary angiogram images based on neighborhood unhomogeneity of degree information.
The method of image segmentation based on graph theory is a new research hotspot in the field of image segmentation, currently. Graph theory is a branch of mathematics, its research object is graph and the graph is regard as a figure constructed by some points and some lines which connect two points. An image can be mapped into a weighted undirected graph, the pixels in the image can be looked as the nodes of graph and the edges can be formed between every pair of nodes. The degree of nodes is an important parameter of undirected graph which is decided by the weight of edges linking to nodes. Considering of the poor connectivity of coronary arteries extracted by existing segmentation algorithm, graph theory is introduced to angiogram segmentation. According to the characteristics of degree information, another measurement parameter of image transition region extraction is constructed which is the neighborhood unhomogeneity of degree information. In the paper, two segmentation methods of coronary angiogram images based on degree information neighborhood unhomogeneity are proposed after exacting the transition region. The two methods improve the quality of tiny vessels extraction and the connectivity of tiny blood vessels on different degree.
引文
[1] Marc S. Angiographic Image Analysis to Assess the Severity of Coroary Stenoses [D]. Netherland: University of Twente, 2002.
[2] Haris K, Efstratiadis S N, Maglaveras N, et al. Automated coronary artery extraction using watersheds [J]. IEEE Computers in Cardiology, 1997, 24: 741-744.
[3]郭继鸿,王伟民.介入性心脏病学[M].北京:中国科学技术出版社, 1991.
[4]徐智.心血管造影图像的二维信息处理及其三维重建研究[D]天津:天津大学光学工程, 2003.
[5]郝聚涛.血管造影图像统计分割研究[D].上海:上海交通大学电子信息与电气工程学院, 2007.
[6]章毓晋.图像处理和分析[M].北京:清华大学出版社, 1999.
[7] Zhang Y J. Segmentation evaluation and comparison: a study of various algorithms [C]. Visual Communications and Image Processing’93, 1993, 2094: 801-812.
[8]孙丰荣,李艳玲,曲怀敬,等.基于活动轮廓模型和边缘对比度特征量的血管内超生图像边缘提取[J].中国生物医学工程学报, 2006, 25(4): 385-389.
[9] Rueckert D, Burger P. Contour fitting using Stochastic and probabilistic relaxation for Cine MR Images [J]. Computer Assisted Radiology, 1995, 137-142.
[10] Pellot C, Herment A, Sigelle M, et al. A 3D reconstruction of vascular structures from two x-ray angiograms using an adapted simulated annealing algorithm [J]. IEEE Transactions on Medical Imaging, 1994, 13(1): 48-60.
[11] Bulpitt A J, Berry E. Spiral CT of abdominal aortic aneurysms: Comparison of segmentation with and automatic 3D deformable model and interactive segmentation [C]. Proc. SPIE, 1998, 3338: 938-946.
[12] Rueckert D, Burger P, Forbat S M, et al. Automatic tracking of the aorta in cardiovascular MR images using deformable models [J]. IEEE Transactions on Medical Imaging, 1997, 16(5): 581-590.
[13] Amini A A, Weymouth T E. Using dynamic programming for solving variational problems in vision [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(9): 855-867.
[14] Cohen L D. On active contour models and balloons [J]. CVGIP: Imaging Understanding, 1991, 53(2): 211-218.
[15] Toledo R, Orriols X, Radeva P. Eigensnakes for vessel segmentation in angiography [C]. 15th International Conference on Pattern Recognition. 2000, 4: 340-343.
[16] Caselles V, Catte F, Coll T, et al. A geometric model for active contours in image processing [J]. Numerische Mathematik, 1993, 66(1): 1-31.
[17] Malladi R, Sethian J A, Vemuri B C. Shape modeling with front propagation: A levelset approach [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(2): 158-175.
[18] Caselles V, Kimmel R, Sapiro G. Geodesic active contours [J]. International Journal of Computer Vision, 1997, 22(1): 61-79.
[19]周寿军,陈武凡,冯前进,等.基于概率跟踪的冠状动脉造影图像的血管树提取[J].电子学报, 2006, 7(34): 1270-1274.
[20] Kirbas C, Quek F K H. Vessel extraction techniques and algorithms a survey [C]. Third IEEE Symposium on BioInformatics and BioEngineering (BIBE’03), 2003, 238-245.
[21] Kirbas C, Quek F K H. A review of vessel extraction techniques and algorithms [J]. ACM Computing Surveys (CSUR), 2004, 36(2): 81-121.
[22]徐智,郁银道,谢洪波,等.造影图像中的心血管边缘提取[J].天津大学学报, 2003, 36(3): 296-299.
[23] Niessen W J, Montauban A D, Elsman B H P, et al. Improved arterial visualization in blood pool agent MRA of the peripheral vasculature [J]. Computer Assisted Radiology and Surgery, 1999: 119-123.
[24] Armande N, Montesinos P, Monga O, et al. Thin Nets Extraction Using a Multi-scale Approach [J]. Computer Vision and Image Understanding, 1999, 73(2): 248-257.
[25] Sato Y, Shiraga N, Nakajima S, et al. Local maximum intensity projection (LMIP): A new rendering method for vascular visualization [J]. Journal of Computer Assisted Tomography, 1998, 22(6): 912-919.
[26] Wilson D L, Nobel J A. Segmentation of cerebral vessels and aneurysms from MR angiography data [C]. Proceedings of the 15th International Conference on Information Processing in Medical Imaging, 1997, 1230: 423-428.
[27] Brien J, Obrien J F, Ezquerra N F. Automatic segmentation of coronary vessels in angiographic image sequences utilizing temporal, spatial, structural constraints [C]. Proceedings of SPIE Visualization in Biomedical Computing, 1994, 25-37.
[28] Schmitt H, Grass M, Rasche V, et al. An x-ray based method for the determination of the contrast agent propagation in 3-D vessel structures [J]. IEEE Transaction on Medical Imaging, 2002, 21(3): 251-262.
[29] Hao X H, Bruce C, Pislaru C, et al. A novel region growing method for segmenting ultrasound images [J]. IEEE Ultrasonics Symposium, 2000, 2: 1717-1720.
[30] Ayala G, Leon T, Zapater V. Different averages of a fuzzy set with an application to vessel segmentation [J]. IEEE Transactions on Fuzzy Systems, 2005, 13(3): 384-393.
[31] Thanapong C, Watcharachai W, Somporn R, et al. Extraction blood vessels from retinal fundus image based on fuzzy C-Median clustering algorithm [C]. Fourth International Conference on Fuzzy Systems and Knowledge Discovery, 2007, 2: 144-148.
[32]刘党辉,沈兰荪,王兴伟.一种角膜新生血管图像的分割方法[J].计算机工程与设计, 2002,23(6): 52-55.
[33] Taleb A, Hmed A, Leclerc X, et al. Semi-automatic segmentation of vessels by mathematical morphology: app-lication in MRI [C]. 2001 International Conference on Image Processing, 2001, 3: 1063-1066.
[34] Passat N, Ronse C, Baruthio J, et al. Automatic parameterization of grey-level hit-or–miss Operators for brain vessel segmentation [C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005, 2: 737-740.
[35] Masutani Y, Kurihara T, Suzuki M, et al. Quantitative vascular shape analysis for 3D MR-angiography using mathematical morphology [C]. Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine, 1995, 905: 449-454.
[36] Stansfiled S A. ANGY: A rule-based expert system for automatic segmentation of coronary vessels from digital subtracted angiograms [J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1986, 8(2): 188-199.
[37] Zhu H Q, Shu H Z, Luo L M. Blood vessels segmentation in retina via wavelet transforms using steerable filters [C]. Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems, 2004: 316-321.
[38] Udupa J K, Samarasekera S. Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation [J]. Graphical Models and Image Processing, 1996, 58(3): 246-261.
[39]姚畅,陈后金,李居朋.基于过渡区提取的视网膜血管分割方法[J].电子学报, 2008, 36(5): 974-978.
[40] Shiffman S, Rubin G D, Napel S, et al. Semiautomated editing of computed tomography sections for visualization of vasculature [C]. Proceedings of SPIE Medical Imaging Conference, 1996, 2707: 140-151.
[41]赵荣椿.数字图像处理导论[M].西安:西北工业大学出版社, 1999.
[42]孙兆林. MATLAB 6.X图像处理[M].北京:清华大学出版社, 2002.
[43] Rosenfeld A, Delatorre P. Histogram concavity analysis as an aid in threshold selection [J]. IEEE Transactions on Systems, Man, and Cybernetics, 1983, SMC-13: 231-235.
[44] Tsai W H. Moment-preserving thresholding: a new approach [J], Computer Vision, Graphics, and Image Processing, 1985, 29: 377-393.
[45] Sahoo P, Wilkings C, Yeager J. Threshold selection using Reny’s entropy [J]. Pattern Recognition, 1997, 30(1): 71-84.
[46]桑农,张天序,曹治国.基于边缘约束的红外目标图像松弛分割技术[J].电子学报, 2002, 30(7): 1027-1031.
[47]戴剑彬,张大力.图象分析中的松弛标记法[J].中国图象图形学报, 1998, 3(2): 97-99.
[48]王润生.图像理解[M].长沙:国防科技大学出版社, 1998.
[49] Zhang Y J, Gerbrands J J. Transition region determination based thresholding [J]. Pattern Recognition Letters, 1991, 12(1): 13-23.
[50]李洪周,袁胜智,陈榕.复杂背景的红外图像过渡区提取与分割[J].激光与红外, 2009, 39(2): 217-219.
[51]闫成新.基于区域的图像分割技术研究[D].武汉:华中科技大学模式识别与智能系统, 2004.
[52]袁胜智,李洪周,杨利斌.基于过渡区的红外目标图像分割与压缩研究[J].激光与红外, 2009, 39(3): 326-329.
[53]王彦春,梁德群,王演.基于图像模糊熵邻域非一致性的过渡区提取与分割[J].电子学报, 2008, 36(12): 2445-2449.
[54]王彦春,梁德群,王演,等.基于邻域非一致性图像过渡区提取与分割[J].光电子·激光, 2008, 19(3): 404-408.
[55] Wilson D L, Noble J A. An adaptive segmentation algorithm for time-of-flight MRA data [J]. IEEE Trans Med Imaging, 1999, 18(10): 938-945.
[56] Guo J K, Chen C H, Lee J Y, et al. 3-D image reconstruction of brain blood vessels from angiogram [J]. Computers and Mathematics with Applications, 1998, 35(8): 79-94.
[57] Witkin A P. Scale space filtering [C]. 8th International Joint Conference Artificial Intelligence, 1983, 2: 1019-1022.
[58] Perona P, Jitendra M. Scale-space and edge detection using anisotropic diffusion [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(7): 629-639.
[59] Koller T M, Gering G, Szekely G, et al. Multiscale detection of curvilinear structures in 2-D and 3-D image data [C]. Fifth International Conference on Computer Vision (ICCV’95), 1995: 864-869.
[60] Frangi A F, Niessen W J, Vincken K L, et al. Multi scale vessel enhancement filtering [C]. Medical Image Computing and Computer-assisted Intervention (MICCAI’98), 1998, 1496: 130-137.
[61] Sato Y, Nakajima S, Shiraga N, et al. Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images [J]. Medical Image Analysis, 1998, 2(2): 143-168.
[62] Sato Y, Westin C , Bhalerao A, et al. Tissue classification based on 3D local intensity structures for volume rending [J]. IEEE Transactions on Visualization and Computer Graphics, 2000, 6(2): 160-180.
[63] Li Q, Sone S, Doi K. Selective enhancement filters for nodules, vessels, and airway walls in two-and three-dimensional CT scans [J]. Medical Physics, 2003, 30(8): 2040-2051.
[64] Agam G, Armato S G, Wu C. Vessel tree reconstruction in thoracic CT scans with application to nodule detection [J]. IEEE Transactions on Medical Imaging, 2005,24(4): 486-499.
[65] Krissian K, Malandain G., Ayache N, et al., Model based multiscale detection of 3D vessels [C]. Proceedings of the IEEE Computer Society Conference on Computer and Vision Pattern Recognition, 1998: 722-727.
[66] Aylward S, Pizer S, Bullitt E, et al. Intensity ridge and widths for tubular object segmentation and description [C]. 1996 Workshop on Mathematical Models in Biomedical Image Analysis (MMBIA’96), 1996: 131-138.
[67]主海文,刘有军,曾衍钧.血管图像分割技术的研究进展[J].北京生物医学工程, 2005, 24(2): 155-159.
[68] Frangi A F, Niessen W J, Hoogeveen R M, et al. Model-based quantitation of 3-D magnetic resonance angiographic images [J]. IEEE Transactions Medical Imaging, 1999, 18(10): 946-956.
[69] Krissian K, Malandain G, Vaillant N A R, et al. Model-based multiscale detection of 3D vessels [C]. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recongition, 1998: 722-727.
[70] Krissian K, Malandain G, Ayache N, et al. Model-based detection of tubular structures in 3D images [J]. Computer Vision and Image Understanding, 2000, 80(2): 130-171.
[71] Yim P J, Cebral J R, Marcos H B, et al. Vessel surface reconstruction with a tubular deformable model [J]. IEEE Transaction on Medical Imaging, 2001, 20(12): 1411-1421.
[72] Aylward S R, Bullitt E. Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction [J]. IEEE Transaction on Medical Imaging, 2002, 21(2): 61-75.
[73] Dehmeshki J, Amin H, Valdivieso M, et al. Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach [J]. IEEE Transactions on Medical Imaging, 2008, 27(4): 467-480.
[74] Brieva J, Gonzalez E, Gonzalez F, et al. A level set method for vessel segmentation in coronary angiography [C]. Conf Proc IEEE Eng Med Biol Soc, 2005, 6: 6348-6351.
[75] Wink O, Niessen W J, Viergever M A. Multiscale vessel tracking [J]. IEEE Transactions on Medical Imaging, 2004, 23(1): 130-133.
[76] Fridman Y, Pizer S M., Aylward S, et al. Extracting branching tubular object geometry via cores [J]. Medical Image Analysis, 2004, 8(3): 169-176.
[77] Volkau I, Weili Z, Baimouratov R, et al. Geometric modeling of the human normal cerebral arterial system [J]. IEEE Transaction on Medical Imaging, 2005, 24(4): 529-539.
[78] Pellot C, Herment A, Sigelle M, et al. A 3D reconstruction of vascular structures from two X-ray angiograms using an adapted simulated annealing algorithm [J]. IEEE Transaction on Medical Imaging, 1994, 13(1): 48-60.
[79]白杨,孙跃,胡银萍,等.蚁群算法在磁共振图像分割中的应用[J].中国医学影像技术, 2007, 23(9): 1402-1404.
[80]王小燕,许建荣.图像分割技术在血管图像中的应用[J].中国介入影像与治疗学, 2009, 6(1): 91-94.
[81]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[J].中国图象图形学报, 2005, 10(12): 1542-1547.
[82]潘立丰,王利生.一种视网膜血管自适应提取方法[J].中国图象图形学报, 2006, 11(3): 310-316.
[83]李抱朴,桑农,曹治国.一种新的血管造影图像增强方法[J].电子学报, 2006, 34(4): 695-697.
[84]徐智,郁银道,谢洪波.心血管造影图像中的心血管提取[J].中国生物医学工程学报, 2003, 22(1): 6-11.
[85]张尤赛,陈福民.三维医学图像的体绘制技术综述[J].计算机工程与应用, 2002, 38(8): 18.
[86] Zhang, Y J. Evaluation and comparison of different segmentation algorithms [J]. Pattern Recognition Letters, 1997, 18(10): 963-974.
[87] Geman S, Geman D. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images [J]. Readings in Uncertain Reasoning, 1990: 452-472.
[88]缪亚林,张红梅,卞正中.小血管医学图像的增强与实时处理[J].西安交通大学学报, 2005, 39(4): 360-363.
[89] Movassaghi B, Rasche V, Viergever M A, et al. Quantitative analysis of 3D coronary moceling in 3D rotational X-ray imaging [C]. Nuclear Science Symposium Conference Record, 2002 IEEE, 2002, 2: 878-880.
[90]杨健,王涌天,唐宋元,等. DSA血管三维重建技术分析与展望[J].中国生物医学工程学报, 2005, 24(6): 655-661.
[91]沈傲东.医学影像三维重建方法研究[D].西安:西安电子科技大学, 2003.
[92] Zhou L, Rzeszotarski M S, L J, Singerman, L J, et al. The detection and quantification of retinopathy using digital angiograms [J]. IEEE Transactions on Medical Imaging, 1994, 13(4): 619-626.
[93] Westenberg J J, vander Geest R J, Wasser M N, et al, Vessel diameter measurements in gadolinium contrast-enhanced three-dimensional MRA of peripheral arteries [J]. Magnetic Resonance Imaging, 2000, 18(1): 13-22.
[94] Chaudhuri S, Chatterjee S, Katz N, et al. Detection of blood vessels in retinal images using two-dimensional matched filters [J]. IEEE Transaction Medical Imaging. 1989, 8(3): 263-269.
[95]荆仁杰,叶秀清,徐胜,等.计算机图象处理[M].浙江:浙江大学出版社, 1990.
[96] Lee J S. Digital image smoothing and the sigma filter [J]. Computer Vision, Graphics, and Image Processing, 1983, 24(2): 255-269.
[97]阮秋琦,阮宇智.数字图像处理[M].北京:电子工业出版社, 2001.
[98]贾云得.机器视觉[M].北京:科学出版社, 2000.
[99]王风化,杨浩.图像增强技术的分析和比较[J].中国空间科学技术, 1992, 3: 47-51.
[100] Eiho S, Qian Y. Detection of Coronary Artery Tree Using Morphological Operator [J]. Computers in Cardiology, 1997, 525-528.
[101]于甬华,田世禹,周正东,等.冠脉数字造影图像血管分割方法研究[J].山东生物医学工程, 2002, 21(4): 5-10.
[102]唐智伟,张辉,胡广书.基于形态学的冠状动脉造影图像多尺度增强算法[J].清华大学学报(自然科学版), 2006, 46(3): 418-420.
[103]陈功,易红,倪中华.基于LOG算法的DSA图像边缘检测[J].仪器仪表学报, December 2006, 27(12): 1641-1646.
[104]王树文,闫成新,张天序等,数学形态学在图像处理中的应用[J].计算机工程与应用, 2004, 32: 89-92.
[105] Yang H B. Segmentation of manmade objects from the natural scenes [J]. Journal of Images and Graphics, 1998, 3(8): 647-650.
[106]许超.形态学准圆结构元素和骨架的研究[J].电子学报,1999, 27(8): 78-81.
[107]许燕,胡广书,耿进朝,等.基于血管平行性和拓扑性的冠脉树分割[J].中国生物医学工程学报, 2007, 26(1): 24-29.
[108]章毓晋.过渡区和分割[J].电子学报, 1996, 24(1): 12-17.
[109]乐宁,梁学军,翁世修.图像过渡区算法及其改进[J].红外与毫米波学报, 2001, 20(3): 211-214.
[110]梁学军,乐宁.基于加强梯度算子的图像过渡区算法[J].图像自动化与识别, 2001(1): 4-7.
[111] Groenewald A M, Bamard E, Botha E C. Related approaches to gradient-based thresholding [J]. Pattern Recognition Letters, 1993, 14(7): 567-572.
[112]冯瑞,冯布云.熵[M].北京:科学出版社出版, 1992.
[113]李月景,图像识别技术及其应用[M].北京:机械工业出版社, 1985.
[114]杨烜,梁德群.基于图像信息测度的多尺度边缘检测方法[J].模式识别与人工智能, 1998, 11(4): 442-446.
[115] Zimmer Y, Tepper R, Akselrod S. A two-dimensional extension of minimum cross entropy thresholding for the segmentation of ultrasound images [J]. Ultrasound in Medicine & Biology, 1996, 22(9): 1183-1190.
[116] Huang S Y, Zhang E H. A method for segmentation of retinal image vessels [C]. Proceedings of the 6th World Congress on Intelligent Control and Automation, 2006, 9673-9676.
[117] Yan C X, Sang N, Zhang T X. Local Entropy-based transition region extraction and thresholding [J]. Pattern Recognition Letters, 2003, 24(16): 2935-2941.
[118]张超,张家树,陈辉.基于局部模糊熵的图像过渡区提取算法[J].西南交通大学学报, 2005, 40(5): 663-666.
[119]闫成新,桑农,张天序,等.基于局部复杂度的图像过渡区提取与分割[J].红外与毫米波学报, 2005, 24(4): 312-316.
[120]曹占辉,张科,李言俊.局部模糊复杂度的图像过渡区提取算法[J].火力与指挥控制, 2008, 33(1): 25-27.
[121] Wu Z, Leahy R. An optimal graph theoretic approach to data clustering: theory and its application to image segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, 15(11): 1101-1113.
[122] Shi J, Malik J. Normalized cuts and image segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905.
[123] Wang S, Siskind J M. Image segmentation with ratio cut [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(6): 675-690.
[124] Sarkar S, Boyer K L. Quantitative measures of change based on feature organization: eigenvalues and eigenvectors [J]. Computer Vision and Image Understanding, 1998, 71(1): 110-136.
[125] Ding C H Q, He X F, Zha H Y, et al, A min-max cut Algorithm for graph partitioning and data clustering [C]. Proceedings of the 2001 IEEE International Conference on Data Mining, 2001: 107-114.
[126] Grady L J. Space-variant computer vision: a graph-theoretic approach [D]. Boston: Boston University, 2004.
[127] Veksler O. Image segmentation by nested cuts [C]. IEEE Conference on Computer Vision and Pattern Recognition, 2000: 339-344.
[128] Perona P, Freeman W T. A factorization approach to grouping [C]. European Conference on Computer Vision, 1999, 1: 655-670.
[129] Ng A Y, Jordan M I, Weiss Y. On spectral clustering: Analysis and algorithm [C]. Advances in Neural Information Processing Systems 14, 2001: 849-856.
[130] Scott G L, Longuet-Higgins H C. Feature grouping by relocalisation of eigenvectors of the proximity matrix [C]. Machine Vision Conference, 1990: 103-108.
[131] Frangakis A S, Hegerl R. Segmentation of biomedical images with eigenvectors [C]. Proceedings of IEEE International Symposium on Biomedical Imaging, 2002: 90-93.
[132] Garballido-Gamio J, Belongie S J, Majumdar S. Normalized cuts in 3-D for spinal MRI segmentation [J]. IEEE Transactions on Medical Imaging, 2004, 23(1): 36-44.
[133] Belongie S, Fowlkes C, Chung F. et al. Spectral partitioning with indefinite kernels using the Nystrom extension [C]. European Conference on Computer Vision 2002, 2002: 531-542.
[134] Yu S X. Computational models of perceptual organization [D]. Pittsburgh, Pennsylvania: Carnegie Mellon University, 2003.
[135] Xu N, Bansal R, Ahuja N. Object segmentation using graph cuts based activecontours [C]. 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, 2.
[136] Malik J, Belongie S, Leung T, et al. Contour and texture analysis for image segmentation [J]. International Journal of Computer Vision, 2001, 43(1): 7-27.
[137] Fowlkes C, Belongie S, Malik J. Efficient spatiotemporal grouping using the Nystrom method [C]. 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, 1: 231-238.
[138] Shi J, Malik J. Motion segmentation and tracking using normalized cuts [C]. International Conference on Computer Vision, 1998: 1154-1160.
[139]杨海军,梁德群.一种新的基于信息测度和神经网络的边缘检测方法[J].电子学报, 2001, 29(1): 51-53.
[140]闫成新,桑农,张天序.基于度信息的图像过渡区提取与分割[J].华中科技大学学报(自然科学版), 2004, 32(10): 1-3.
[141]才辉,张光新,张浩,等.一种新的基于多信息测度融合的边缘检测方法[J].浙江大学学报(工学版), 2008, 42(10): 1671-1675.
[142] Yen J C, Chang F J, Chang S. A New criterion for automatic multilevel thresholding [J]. IEEE Transactions on Image Processing, 1995, 4(3): 370-378.
[143]朱宏擎.基于灰度-梯度共生矩阵的视网膜血管分割方法[J].上海交通大学学报, 2004, 38(9): 1485-1488.
[2] Haris K, Efstratiadis S N, Maglaveras N, et al. Automated coronary artery extraction using watersheds [J]. IEEE Computers in Cardiology, 1997, 24: 741-744.
[3]郭继鸿,王伟民.介入性心脏病学[M].北京:中国科学技术出版社, 1991.
[4]徐智.心血管造影图像的二维信息处理及其三维重建研究[D]天津:天津大学光学工程, 2003.
[5]郝聚涛.血管造影图像统计分割研究[D].上海:上海交通大学电子信息与电气工程学院, 2007.
[6]章毓晋.图像处理和分析[M].北京:清华大学出版社, 1999.
[7] Zhang Y J. Segmentation evaluation and comparison: a study of various algorithms [C]. Visual Communications and Image Processing’93, 1993, 2094: 801-812.
[8]孙丰荣,李艳玲,曲怀敬,等.基于活动轮廓模型和边缘对比度特征量的血管内超生图像边缘提取[J].中国生物医学工程学报, 2006, 25(4): 385-389.
[9] Rueckert D, Burger P. Contour fitting using Stochastic and probabilistic relaxation for Cine MR Images [J]. Computer Assisted Radiology, 1995, 137-142.
[10] Pellot C, Herment A, Sigelle M, et al. A 3D reconstruction of vascular structures from two x-ray angiograms using an adapted simulated annealing algorithm [J]. IEEE Transactions on Medical Imaging, 1994, 13(1): 48-60.
[11] Bulpitt A J, Berry E. Spiral CT of abdominal aortic aneurysms: Comparison of segmentation with and automatic 3D deformable model and interactive segmentation [C]. Proc. SPIE, 1998, 3338: 938-946.
[12] Rueckert D, Burger P, Forbat S M, et al. Automatic tracking of the aorta in cardiovascular MR images using deformable models [J]. IEEE Transactions on Medical Imaging, 1997, 16(5): 581-590.
[13] Amini A A, Weymouth T E. Using dynamic programming for solving variational problems in vision [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(9): 855-867.
[14] Cohen L D. On active contour models and balloons [J]. CVGIP: Imaging Understanding, 1991, 53(2): 211-218.
[15] Toledo R, Orriols X, Radeva P. Eigensnakes for vessel segmentation in angiography [C]. 15th International Conference on Pattern Recognition. 2000, 4: 340-343.
[16] Caselles V, Catte F, Coll T, et al. A geometric model for active contours in image processing [J]. Numerische Mathematik, 1993, 66(1): 1-31.
[17] Malladi R, Sethian J A, Vemuri B C. Shape modeling with front propagation: A levelset approach [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1995, 17(2): 158-175.
[18] Caselles V, Kimmel R, Sapiro G. Geodesic active contours [J]. International Journal of Computer Vision, 1997, 22(1): 61-79.
[19]周寿军,陈武凡,冯前进,等.基于概率跟踪的冠状动脉造影图像的血管树提取[J].电子学报, 2006, 7(34): 1270-1274.
[20] Kirbas C, Quek F K H. Vessel extraction techniques and algorithms a survey [C]. Third IEEE Symposium on BioInformatics and BioEngineering (BIBE’03), 2003, 238-245.
[21] Kirbas C, Quek F K H. A review of vessel extraction techniques and algorithms [J]. ACM Computing Surveys (CSUR), 2004, 36(2): 81-121.
[22]徐智,郁银道,谢洪波,等.造影图像中的心血管边缘提取[J].天津大学学报, 2003, 36(3): 296-299.
[23] Niessen W J, Montauban A D, Elsman B H P, et al. Improved arterial visualization in blood pool agent MRA of the peripheral vasculature [J]. Computer Assisted Radiology and Surgery, 1999: 119-123.
[24] Armande N, Montesinos P, Monga O, et al. Thin Nets Extraction Using a Multi-scale Approach [J]. Computer Vision and Image Understanding, 1999, 73(2): 248-257.
[25] Sato Y, Shiraga N, Nakajima S, et al. Local maximum intensity projection (LMIP): A new rendering method for vascular visualization [J]. Journal of Computer Assisted Tomography, 1998, 22(6): 912-919.
[26] Wilson D L, Nobel J A. Segmentation of cerebral vessels and aneurysms from MR angiography data [C]. Proceedings of the 15th International Conference on Information Processing in Medical Imaging, 1997, 1230: 423-428.
[27] Brien J, Obrien J F, Ezquerra N F. Automatic segmentation of coronary vessels in angiographic image sequences utilizing temporal, spatial, structural constraints [C]. Proceedings of SPIE Visualization in Biomedical Computing, 1994, 25-37.
[28] Schmitt H, Grass M, Rasche V, et al. An x-ray based method for the determination of the contrast agent propagation in 3-D vessel structures [J]. IEEE Transaction on Medical Imaging, 2002, 21(3): 251-262.
[29] Hao X H, Bruce C, Pislaru C, et al. A novel region growing method for segmenting ultrasound images [J]. IEEE Ultrasonics Symposium, 2000, 2: 1717-1720.
[30] Ayala G, Leon T, Zapater V. Different averages of a fuzzy set with an application to vessel segmentation [J]. IEEE Transactions on Fuzzy Systems, 2005, 13(3): 384-393.
[31] Thanapong C, Watcharachai W, Somporn R, et al. Extraction blood vessels from retinal fundus image based on fuzzy C-Median clustering algorithm [C]. Fourth International Conference on Fuzzy Systems and Knowledge Discovery, 2007, 2: 144-148.
[32]刘党辉,沈兰荪,王兴伟.一种角膜新生血管图像的分割方法[J].计算机工程与设计, 2002,23(6): 52-55.
[33] Taleb A, Hmed A, Leclerc X, et al. Semi-automatic segmentation of vessels by mathematical morphology: app-lication in MRI [C]. 2001 International Conference on Image Processing, 2001, 3: 1063-1066.
[34] Passat N, Ronse C, Baruthio J, et al. Automatic parameterization of grey-level hit-or–miss Operators for brain vessel segmentation [C]. IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005, 2: 737-740.
[35] Masutani Y, Kurihara T, Suzuki M, et al. Quantitative vascular shape analysis for 3D MR-angiography using mathematical morphology [C]. Proceedings of the First International Conference on Computer Vision, Virtual Reality and Robotics in Medicine, 1995, 905: 449-454.
[36] Stansfiled S A. ANGY: A rule-based expert system for automatic segmentation of coronary vessels from digital subtracted angiograms [J]. IEEE Transaction on Pattern Analysis and Machine Intelligence, 1986, 8(2): 188-199.
[37] Zhu H Q, Shu H Z, Luo L M. Blood vessels segmentation in retina via wavelet transforms using steerable filters [C]. Proceedings of the 17th IEEE Symposium on Computer-Based Medical Systems, 2004: 316-321.
[38] Udupa J K, Samarasekera S. Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation [J]. Graphical Models and Image Processing, 1996, 58(3): 246-261.
[39]姚畅,陈后金,李居朋.基于过渡区提取的视网膜血管分割方法[J].电子学报, 2008, 36(5): 974-978.
[40] Shiffman S, Rubin G D, Napel S, et al. Semiautomated editing of computed tomography sections for visualization of vasculature [C]. Proceedings of SPIE Medical Imaging Conference, 1996, 2707: 140-151.
[41]赵荣椿.数字图像处理导论[M].西安:西北工业大学出版社, 1999.
[42]孙兆林. MATLAB 6.X图像处理[M].北京:清华大学出版社, 2002.
[43] Rosenfeld A, Delatorre P. Histogram concavity analysis as an aid in threshold selection [J]. IEEE Transactions on Systems, Man, and Cybernetics, 1983, SMC-13: 231-235.
[44] Tsai W H. Moment-preserving thresholding: a new approach [J], Computer Vision, Graphics, and Image Processing, 1985, 29: 377-393.
[45] Sahoo P, Wilkings C, Yeager J. Threshold selection using Reny’s entropy [J]. Pattern Recognition, 1997, 30(1): 71-84.
[46]桑农,张天序,曹治国.基于边缘约束的红外目标图像松弛分割技术[J].电子学报, 2002, 30(7): 1027-1031.
[47]戴剑彬,张大力.图象分析中的松弛标记法[J].中国图象图形学报, 1998, 3(2): 97-99.
[48]王润生.图像理解[M].长沙:国防科技大学出版社, 1998.
[49] Zhang Y J, Gerbrands J J. Transition region determination based thresholding [J]. Pattern Recognition Letters, 1991, 12(1): 13-23.
[50]李洪周,袁胜智,陈榕.复杂背景的红外图像过渡区提取与分割[J].激光与红外, 2009, 39(2): 217-219.
[51]闫成新.基于区域的图像分割技术研究[D].武汉:华中科技大学模式识别与智能系统, 2004.
[52]袁胜智,李洪周,杨利斌.基于过渡区的红外目标图像分割与压缩研究[J].激光与红外, 2009, 39(3): 326-329.
[53]王彦春,梁德群,王演.基于图像模糊熵邻域非一致性的过渡区提取与分割[J].电子学报, 2008, 36(12): 2445-2449.
[54]王彦春,梁德群,王演,等.基于邻域非一致性图像过渡区提取与分割[J].光电子·激光, 2008, 19(3): 404-408.
[55] Wilson D L, Noble J A. An adaptive segmentation algorithm for time-of-flight MRA data [J]. IEEE Trans Med Imaging, 1999, 18(10): 938-945.
[56] Guo J K, Chen C H, Lee J Y, et al. 3-D image reconstruction of brain blood vessels from angiogram [J]. Computers and Mathematics with Applications, 1998, 35(8): 79-94.
[57] Witkin A P. Scale space filtering [C]. 8th International Joint Conference Artificial Intelligence, 1983, 2: 1019-1022.
[58] Perona P, Jitendra M. Scale-space and edge detection using anisotropic diffusion [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(7): 629-639.
[59] Koller T M, Gering G, Szekely G, et al. Multiscale detection of curvilinear structures in 2-D and 3-D image data [C]. Fifth International Conference on Computer Vision (ICCV’95), 1995: 864-869.
[60] Frangi A F, Niessen W J, Vincken K L, et al. Multi scale vessel enhancement filtering [C]. Medical Image Computing and Computer-assisted Intervention (MICCAI’98), 1998, 1496: 130-137.
[61] Sato Y, Nakajima S, Shiraga N, et al. Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images [J]. Medical Image Analysis, 1998, 2(2): 143-168.
[62] Sato Y, Westin C , Bhalerao A, et al. Tissue classification based on 3D local intensity structures for volume rending [J]. IEEE Transactions on Visualization and Computer Graphics, 2000, 6(2): 160-180.
[63] Li Q, Sone S, Doi K. Selective enhancement filters for nodules, vessels, and airway walls in two-and three-dimensional CT scans [J]. Medical Physics, 2003, 30(8): 2040-2051.
[64] Agam G, Armato S G, Wu C. Vessel tree reconstruction in thoracic CT scans with application to nodule detection [J]. IEEE Transactions on Medical Imaging, 2005,24(4): 486-499.
[65] Krissian K, Malandain G., Ayache N, et al., Model based multiscale detection of 3D vessels [C]. Proceedings of the IEEE Computer Society Conference on Computer and Vision Pattern Recognition, 1998: 722-727.
[66] Aylward S, Pizer S, Bullitt E, et al. Intensity ridge and widths for tubular object segmentation and description [C]. 1996 Workshop on Mathematical Models in Biomedical Image Analysis (MMBIA’96), 1996: 131-138.
[67]主海文,刘有军,曾衍钧.血管图像分割技术的研究进展[J].北京生物医学工程, 2005, 24(2): 155-159.
[68] Frangi A F, Niessen W J, Hoogeveen R M, et al. Model-based quantitation of 3-D magnetic resonance angiographic images [J]. IEEE Transactions Medical Imaging, 1999, 18(10): 946-956.
[69] Krissian K, Malandain G, Vaillant N A R, et al. Model-based multiscale detection of 3D vessels [C]. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recongition, 1998: 722-727.
[70] Krissian K, Malandain G, Ayache N, et al. Model-based detection of tubular structures in 3D images [J]. Computer Vision and Image Understanding, 2000, 80(2): 130-171.
[71] Yim P J, Cebral J R, Marcos H B, et al. Vessel surface reconstruction with a tubular deformable model [J]. IEEE Transaction on Medical Imaging, 2001, 20(12): 1411-1421.
[72] Aylward S R, Bullitt E. Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction [J]. IEEE Transaction on Medical Imaging, 2002, 21(2): 61-75.
[73] Dehmeshki J, Amin H, Valdivieso M, et al. Segmentation of pulmonary nodules in thoracic CT scans: a region growing approach [J]. IEEE Transactions on Medical Imaging, 2008, 27(4): 467-480.
[74] Brieva J, Gonzalez E, Gonzalez F, et al. A level set method for vessel segmentation in coronary angiography [C]. Conf Proc IEEE Eng Med Biol Soc, 2005, 6: 6348-6351.
[75] Wink O, Niessen W J, Viergever M A. Multiscale vessel tracking [J]. IEEE Transactions on Medical Imaging, 2004, 23(1): 130-133.
[76] Fridman Y, Pizer S M., Aylward S, et al. Extracting branching tubular object geometry via cores [J]. Medical Image Analysis, 2004, 8(3): 169-176.
[77] Volkau I, Weili Z, Baimouratov R, et al. Geometric modeling of the human normal cerebral arterial system [J]. IEEE Transaction on Medical Imaging, 2005, 24(4): 529-539.
[78] Pellot C, Herment A, Sigelle M, et al. A 3D reconstruction of vascular structures from two X-ray angiograms using an adapted simulated annealing algorithm [J]. IEEE Transaction on Medical Imaging, 1994, 13(1): 48-60.
[79]白杨,孙跃,胡银萍,等.蚁群算法在磁共振图像分割中的应用[J].中国医学影像技术, 2007, 23(9): 1402-1404.
[80]王小燕,许建荣.图像分割技术在血管图像中的应用[J].中国介入影像与治疗学, 2009, 6(1): 91-94.
[81]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[J].中国图象图形学报, 2005, 10(12): 1542-1547.
[82]潘立丰,王利生.一种视网膜血管自适应提取方法[J].中国图象图形学报, 2006, 11(3): 310-316.
[83]李抱朴,桑农,曹治国.一种新的血管造影图像增强方法[J].电子学报, 2006, 34(4): 695-697.
[84]徐智,郁银道,谢洪波.心血管造影图像中的心血管提取[J].中国生物医学工程学报, 2003, 22(1): 6-11.
[85]张尤赛,陈福民.三维医学图像的体绘制技术综述[J].计算机工程与应用, 2002, 38(8): 18.
[86] Zhang, Y J. Evaluation and comparison of different segmentation algorithms [J]. Pattern Recognition Letters, 1997, 18(10): 963-974.
[87] Geman S, Geman D. Stochastic relaxation, gibbs distributions, and the bayesian restoration of images [J]. Readings in Uncertain Reasoning, 1990: 452-472.
[88]缪亚林,张红梅,卞正中.小血管医学图像的增强与实时处理[J].西安交通大学学报, 2005, 39(4): 360-363.
[89] Movassaghi B, Rasche V, Viergever M A, et al. Quantitative analysis of 3D coronary moceling in 3D rotational X-ray imaging [C]. Nuclear Science Symposium Conference Record, 2002 IEEE, 2002, 2: 878-880.
[90]杨健,王涌天,唐宋元,等. DSA血管三维重建技术分析与展望[J].中国生物医学工程学报, 2005, 24(6): 655-661.
[91]沈傲东.医学影像三维重建方法研究[D].西安:西安电子科技大学, 2003.
[92] Zhou L, Rzeszotarski M S, L J, Singerman, L J, et al. The detection and quantification of retinopathy using digital angiograms [J]. IEEE Transactions on Medical Imaging, 1994, 13(4): 619-626.
[93] Westenberg J J, vander Geest R J, Wasser M N, et al, Vessel diameter measurements in gadolinium contrast-enhanced three-dimensional MRA of peripheral arteries [J]. Magnetic Resonance Imaging, 2000, 18(1): 13-22.
[94] Chaudhuri S, Chatterjee S, Katz N, et al. Detection of blood vessels in retinal images using two-dimensional matched filters [J]. IEEE Transaction Medical Imaging. 1989, 8(3): 263-269.
[95]荆仁杰,叶秀清,徐胜,等.计算机图象处理[M].浙江:浙江大学出版社, 1990.
[96] Lee J S. Digital image smoothing and the sigma filter [J]. Computer Vision, Graphics, and Image Processing, 1983, 24(2): 255-269.
[97]阮秋琦,阮宇智.数字图像处理[M].北京:电子工业出版社, 2001.
[98]贾云得.机器视觉[M].北京:科学出版社, 2000.
[99]王风化,杨浩.图像增强技术的分析和比较[J].中国空间科学技术, 1992, 3: 47-51.
[100] Eiho S, Qian Y. Detection of Coronary Artery Tree Using Morphological Operator [J]. Computers in Cardiology, 1997, 525-528.
[101]于甬华,田世禹,周正东,等.冠脉数字造影图像血管分割方法研究[J].山东生物医学工程, 2002, 21(4): 5-10.
[102]唐智伟,张辉,胡广书.基于形态学的冠状动脉造影图像多尺度增强算法[J].清华大学学报(自然科学版), 2006, 46(3): 418-420.
[103]陈功,易红,倪中华.基于LOG算法的DSA图像边缘检测[J].仪器仪表学报, December 2006, 27(12): 1641-1646.
[104]王树文,闫成新,张天序等,数学形态学在图像处理中的应用[J].计算机工程与应用, 2004, 32: 89-92.
[105] Yang H B. Segmentation of manmade objects from the natural scenes [J]. Journal of Images and Graphics, 1998, 3(8): 647-650.
[106]许超.形态学准圆结构元素和骨架的研究[J].电子学报,1999, 27(8): 78-81.
[107]许燕,胡广书,耿进朝,等.基于血管平行性和拓扑性的冠脉树分割[J].中国生物医学工程学报, 2007, 26(1): 24-29.
[108]章毓晋.过渡区和分割[J].电子学报, 1996, 24(1): 12-17.
[109]乐宁,梁学军,翁世修.图像过渡区算法及其改进[J].红外与毫米波学报, 2001, 20(3): 211-214.
[110]梁学军,乐宁.基于加强梯度算子的图像过渡区算法[J].图像自动化与识别, 2001(1): 4-7.
[111] Groenewald A M, Bamard E, Botha E C. Related approaches to gradient-based thresholding [J]. Pattern Recognition Letters, 1993, 14(7): 567-572.
[112]冯瑞,冯布云.熵[M].北京:科学出版社出版, 1992.
[113]李月景,图像识别技术及其应用[M].北京:机械工业出版社, 1985.
[114]杨烜,梁德群.基于图像信息测度的多尺度边缘检测方法[J].模式识别与人工智能, 1998, 11(4): 442-446.
[115] Zimmer Y, Tepper R, Akselrod S. A two-dimensional extension of minimum cross entropy thresholding for the segmentation of ultrasound images [J]. Ultrasound in Medicine & Biology, 1996, 22(9): 1183-1190.
[116] Huang S Y, Zhang E H. A method for segmentation of retinal image vessels [C]. Proceedings of the 6th World Congress on Intelligent Control and Automation, 2006, 9673-9676.
[117] Yan C X, Sang N, Zhang T X. Local Entropy-based transition region extraction and thresholding [J]. Pattern Recognition Letters, 2003, 24(16): 2935-2941.
[118]张超,张家树,陈辉.基于局部模糊熵的图像过渡区提取算法[J].西南交通大学学报, 2005, 40(5): 663-666.
[119]闫成新,桑农,张天序,等.基于局部复杂度的图像过渡区提取与分割[J].红外与毫米波学报, 2005, 24(4): 312-316.
[120]曹占辉,张科,李言俊.局部模糊复杂度的图像过渡区提取算法[J].火力与指挥控制, 2008, 33(1): 25-27.
[121] Wu Z, Leahy R. An optimal graph theoretic approach to data clustering: theory and its application to image segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1993, 15(11): 1101-1113.
[122] Shi J, Malik J. Normalized cuts and image segmentation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(8): 888-905.
[123] Wang S, Siskind J M. Image segmentation with ratio cut [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2003, 25(6): 675-690.
[124] Sarkar S, Boyer K L. Quantitative measures of change based on feature organization: eigenvalues and eigenvectors [J]. Computer Vision and Image Understanding, 1998, 71(1): 110-136.
[125] Ding C H Q, He X F, Zha H Y, et al, A min-max cut Algorithm for graph partitioning and data clustering [C]. Proceedings of the 2001 IEEE International Conference on Data Mining, 2001: 107-114.
[126] Grady L J. Space-variant computer vision: a graph-theoretic approach [D]. Boston: Boston University, 2004.
[127] Veksler O. Image segmentation by nested cuts [C]. IEEE Conference on Computer Vision and Pattern Recognition, 2000: 339-344.
[128] Perona P, Freeman W T. A factorization approach to grouping [C]. European Conference on Computer Vision, 1999, 1: 655-670.
[129] Ng A Y, Jordan M I, Weiss Y. On spectral clustering: Analysis and algorithm [C]. Advances in Neural Information Processing Systems 14, 2001: 849-856.
[130] Scott G L, Longuet-Higgins H C. Feature grouping by relocalisation of eigenvectors of the proximity matrix [C]. Machine Vision Conference, 1990: 103-108.
[131] Frangakis A S, Hegerl R. Segmentation of biomedical images with eigenvectors [C]. Proceedings of IEEE International Symposium on Biomedical Imaging, 2002: 90-93.
[132] Garballido-Gamio J, Belongie S J, Majumdar S. Normalized cuts in 3-D for spinal MRI segmentation [J]. IEEE Transactions on Medical Imaging, 2004, 23(1): 36-44.
[133] Belongie S, Fowlkes C, Chung F. et al. Spectral partitioning with indefinite kernels using the Nystrom extension [C]. European Conference on Computer Vision 2002, 2002: 531-542.
[134] Yu S X. Computational models of perceptual organization [D]. Pittsburgh, Pennsylvania: Carnegie Mellon University, 2003.
[135] Xu N, Bansal R, Ahuja N. Object segmentation using graph cuts based activecontours [C]. 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003, 2.
[136] Malik J, Belongie S, Leung T, et al. Contour and texture analysis for image segmentation [J]. International Journal of Computer Vision, 2001, 43(1): 7-27.
[137] Fowlkes C, Belongie S, Malik J. Efficient spatiotemporal grouping using the Nystrom method [C]. 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2001, 1: 231-238.
[138] Shi J, Malik J. Motion segmentation and tracking using normalized cuts [C]. International Conference on Computer Vision, 1998: 1154-1160.
[139]杨海军,梁德群.一种新的基于信息测度和神经网络的边缘检测方法[J].电子学报, 2001, 29(1): 51-53.
[140]闫成新,桑农,张天序.基于度信息的图像过渡区提取与分割[J].华中科技大学学报(自然科学版), 2004, 32(10): 1-3.
[141]才辉,张光新,张浩,等.一种新的基于多信息测度融合的边缘检测方法[J].浙江大学学报(工学版), 2008, 42(10): 1671-1675.
[142] Yen J C, Chang F J, Chang S. A New criterion for automatic multilevel thresholding [J]. IEEE Transactions on Image Processing, 1995, 4(3): 370-378.
[143]朱宏擎.基于灰度-梯度共生矩阵的视网膜血管分割方法[J].上海交通大学学报, 2004, 38(9): 1485-1488.