基于CT造影图像的肺栓塞计算机辅助检测
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摘要
肺栓塞在西方国家被列为常见的三种心血管疾病之一,具有诊断率低、死亡率高的特点。在中国,肺栓塞长期以来被误认为是一种罕见病,被临床医生严重忽视,但近年来逐渐引起医学界的重视并广泛普及。肺栓塞在临床上无特异性,极易被忽视,未及时诊断和有效诊治会导致大部分的栓塞患者死亡。计算机断层扫描与血管造影技术的出现使现代影像学检查技术成为肺栓塞检测诊断的重要手段,尤其是CT肺动脉造影技术已经成为临床上检测肺栓塞的一种主要途径,配合使用计算机辅助检测技术(CAD)可以进一步提高检查栓塞的准确率和效率。
肺栓塞CAD系统的研究经历了近十年,但是远未成熟,在理论和实际应用中都还有许多亟待解决的问题,在国内更是尚未引起研究者的重视。本文基于肺栓塞CAD系统的常规流程,提出了一套新颖的构建肺栓塞CAD系统的技术路线,该路线将纵膈内部的中心型肺栓塞也纳入重点检测对象,并致力于降低肺栓塞检测的假阳性。本文的研究按照CAD系统的技术路线展开,对各步骤进行了深入的研究,所完成的主要研究工作和创新点如下:
①为得到左右肺分离的完整肺区域,提出了一种基于解剖学知识的3D肺区域自动分割算法。首先,分析CT图像各组织密度分布采用阈值法进行预分割。其次,针对支气管导致肺区域分割误差的问题,提出采用自顶向下的强规则区域生长法提取气道内腔以避免气道渗漏至肺实质,再膨胀至气道壁,然后提出区域面积分析法和投影法以判断与分离左右肺,并进行肺实质边界修补。实验验证了该算法对CT增强扫描和普通扫描数据的适用性,能有效排除两肺间的气管/支气管通道,在复杂图像的分割实验中也取得了良好的效果。
②作为纵膈内肺动脉分割和中心型肺栓塞的工作基础,本文首次将纵膈区域的分割加入肺栓塞CAD系统,并提出了一种3D纵膈区域自动分割算法。纵膈是一个解剖区域,无明显的边界和形状特征,故提出缘点的概念用以描述2D纵膈区域的解剖学结构,并采用基于角点检测的方法提取缘点和基于快速行进的方法提取缘点间路径,同时在2D纵膈分割过程中引入上下文轮廓的位置信息以约束和保障3D纵膈表面的连续性。此外,用与金标准结果间的偏差距离累积分布来客观评估分割效果。实验结果显示,本文基于上下文轮廓的3D纵膈区域自动分割算法能得到完整且平滑的纵膈区域,分割具有良好的准确率和效率。
③为了能够在肺动脉中更精确地检测栓塞,提出一种由纵膈到外围的肺动脉分割方案,将常规的肺血管分割与动静脉分离两个任务集为一体。针对跟踪过程中的渗漏问题和血管中断问题,提出了相应策略。提出基于区域生长和分段行进的血管树跟踪算法作为肺动脉跟踪的主线。针对渗漏问题,提出一种基于多尺度二阶导数的特征图过滤技术以排除或减少相邻对象间的大面积接触区域,并结合3D形态学血管分枝评估技术防止渗漏。对于血管中断部分则通过提取最小代价路径进行补偿。实验结果表明,本方案对纵膈内主肺动脉的分割具有很高敏感度和鲁棒性,能有效排除大部分肺部静脉,同时证实了方案中各技术的有效性。
④针对CTA图像中肺栓塞检测的医学特性,在实现前人研究成果的基础上,提取候选栓塞的不同区域对象并另提出了与周围环境的灰度差异特征、边界灰度特征、部分形态特征以及与基于肺动脉树的特征,用以描述肺栓塞与相关环境及器官对象之间的联系。对各特征的检验概率分析表明本文提取的特征大部分具有更好的真实栓塞识别能力。
⑤联合使用各类特征选择最优特征子集并优化分类器以改善肺栓塞计算机辅助检测系统的性能。首先提出一种基于蚁群算法的组合式特征选择算法,再选择分类器分别评估不同类别特征、特征选择子集、与人工筛选特征子集以筛选出分类性能最优的特征子集。在此基础上,进一步提出采用集成学习的方法改善分类器的性能。实验结果表明采用基于蚁群算法得到的特征子集,结合以随机森林为基分类器的集成学习算法能获得良好的分类性能。与其他肺栓塞CAD系统的性能比较说明本文所提出的CAD系统能够检测中心型、叶段、段以及亚段动脉中的栓塞,同时具有相对很低的假阳性率。
Featured by low rates of diagnosis and high motality rates, pulmonary embolism (PE) is one of the three most common cardiovascular diseases in Western countries. In China, it has long been mistakenly regarded as one of the rare diseases, but is gathering attention rapidly in recent years. Due to its lack of clinical specificities, PE could be easily neglected in clinical diagnosis. The mortality rate is high without early diagnosis and efficient treatment. The invention of computed tomography and angiography has made modern imaging examination a significant method for PE diagnosis. Amongst, CT pulmonary arteriography has been used as one of the main methods of clinical diagnosis. The assistance by computer-aided detection(CAD) system could further enhance accuracy and efficiency of PE diagnosis.
The research on PE CAD system incepted nearly ten years ago. However, it is still far from mature, with many issues remaining to be resolved in both theory and application. In China, the research is primitive at best. The current research, by reference to the normal procedures of PE CAD systems, proposes a new technical route for the designing of PE CAD systems. It takes into account central PE inside mediastinum as one of the key detection objects, and aims to reduce false positives in PE diagnosis. The tasks and innovations made at each step of the research are as follows:
Firstly, an automatic 3D lung segmentation algorithm, based on anatomy knowledge, is proposed with an aim to segment the left and right lungs. At first, pre-segmentation is made by thresholding method, after an analysis of the density distribution of tissues in CT images. With regards segmentation errors caused by bronchus, the top-to-down region growing method is proposed to extract airway lumen and to avoid leakage from airway to parenchyma, to dilate to include airway wall; then the methods of regional area analysis and area projection are proposed to judge and separate left and right lungs, then to repair the edges of parenchyma. Experimental results show the applicability of the algorithm to CT plain scan/enhancement scan data, the effectiveness in excluding airways between the two lungs and in the segmentation of complicated images.
Secondly, in order to provide a basis for segmentation of pulmonary artery inside mediastinum and diagnosis of central PE, the study, in an innovative fashion, adds the module of mediastinum segmentation to PE CAD system and proposes an automatic 3D mediastinum segmentation algorithm. As an anatomic region, the mediastinum does not have clear boundaries or shape. The study uses the idea of margin points in the description of the anatomical structure of 2D mediastinal area. The points are extracted by a method based on corner detection, and the paths between points by fast marching method. Meanwhile, the location information of context-contour is introduced in 2D segmentation process in order to limit and guarantee the continuity of the surface of 3D mediastinum. The segmentation result is evaluated by the cumulative distribution of deviation distance from the gold standard result. The experiment shows that the automatic segmentation algorithm based on context-contour mediastinum could achieve complete and smooth mediastinum, and the segmentation is accurate and efficient.
Thirdly, with an aim to detect PE in the pulmonary artery more accurately, the study proposes a segmentation plan from mediastinal to peripheral pulmonary artery, thus combining the tasks of traditional segmentation of pulmonary vascular and separation of artery and vein. With regards the problem of leakage and unconnected vessels in the tracing process, the following proposals are made: a vascular tree tracking method based on regional growing and slice marching is used for artery tracking; a characteristic pattern filtration method based on multiple-dimensioned second-order derivative is applied in order to eliminate or reduce the contact area of neighboring objects and to prevent leakage by combination with 3D morphological vessel section assessment technique; the unconnected vessels are repaired by extracting minimum cost path. Experimental results show the high sensitivity and robustness for segmentation of main pulmonary artery inside mediastinum. It is also proved that the proposed method could exclude most pulmonary veins effectively.
Forthly, with regards the medical characteristics of PE detection in CTPA images, the present research extracts different regions for candidate PE, based on existing research results, and proposes another eighteen features for describing the relationship between PE and related environment and organs, such as gray-level difference from surrounding environment, gray level of borders, partial morphological feature and feature based on pulmonary artery tree. The chi-square test shows that most of the proposed features have better detection capability for real PE.
Fifthly, the performance of the CAD system for PE diagnosis is improved by selecting an optimal subset among the proposed features and optimizing the classifier. The research proposes a hybrid feature selection algorithm based on ant colony algorithm. Then selected classifiers are used to evaluate features of different types, feature selection subsets and manually classified feature subsets, in order to acquire the optimal feature subset for classification. The performance of classifier is then improved by the method of ensemble learning. Experimental results show the satisfactory performance of the combination of the feature subset deriving from ant colony algorithm and the ensemble learning algorithm with random forest as the base classifier. A comparison of the performance of CAD systems for PE detection reveals that the proposed CAD system could detect central, segmental, section and sub-segmental embolism, and could reduce false positives.
肺栓塞CAD系统的研究经历了近十年,但是远未成熟,在理论和实际应用中都还有许多亟待解决的问题,在国内更是尚未引起研究者的重视。本文基于肺栓塞CAD系统的常规流程,提出了一套新颖的构建肺栓塞CAD系统的技术路线,该路线将纵膈内部的中心型肺栓塞也纳入重点检测对象,并致力于降低肺栓塞检测的假阳性。本文的研究按照CAD系统的技术路线展开,对各步骤进行了深入的研究,所完成的主要研究工作和创新点如下:
①为得到左右肺分离的完整肺区域,提出了一种基于解剖学知识的3D肺区域自动分割算法。首先,分析CT图像各组织密度分布采用阈值法进行预分割。其次,针对支气管导致肺区域分割误差的问题,提出采用自顶向下的强规则区域生长法提取气道内腔以避免气道渗漏至肺实质,再膨胀至气道壁,然后提出区域面积分析法和投影法以判断与分离左右肺,并进行肺实质边界修补。实验验证了该算法对CT增强扫描和普通扫描数据的适用性,能有效排除两肺间的气管/支气管通道,在复杂图像的分割实验中也取得了良好的效果。
②作为纵膈内肺动脉分割和中心型肺栓塞的工作基础,本文首次将纵膈区域的分割加入肺栓塞CAD系统,并提出了一种3D纵膈区域自动分割算法。纵膈是一个解剖区域,无明显的边界和形状特征,故提出缘点的概念用以描述2D纵膈区域的解剖学结构,并采用基于角点检测的方法提取缘点和基于快速行进的方法提取缘点间路径,同时在2D纵膈分割过程中引入上下文轮廓的位置信息以约束和保障3D纵膈表面的连续性。此外,用与金标准结果间的偏差距离累积分布来客观评估分割效果。实验结果显示,本文基于上下文轮廓的3D纵膈区域自动分割算法能得到完整且平滑的纵膈区域,分割具有良好的准确率和效率。
③为了能够在肺动脉中更精确地检测栓塞,提出一种由纵膈到外围的肺动脉分割方案,将常规的肺血管分割与动静脉分离两个任务集为一体。针对跟踪过程中的渗漏问题和血管中断问题,提出了相应策略。提出基于区域生长和分段行进的血管树跟踪算法作为肺动脉跟踪的主线。针对渗漏问题,提出一种基于多尺度二阶导数的特征图过滤技术以排除或减少相邻对象间的大面积接触区域,并结合3D形态学血管分枝评估技术防止渗漏。对于血管中断部分则通过提取最小代价路径进行补偿。实验结果表明,本方案对纵膈内主肺动脉的分割具有很高敏感度和鲁棒性,能有效排除大部分肺部静脉,同时证实了方案中各技术的有效性。
④针对CTA图像中肺栓塞检测的医学特性,在实现前人研究成果的基础上,提取候选栓塞的不同区域对象并另提出了与周围环境的灰度差异特征、边界灰度特征、部分形态特征以及与基于肺动脉树的特征,用以描述肺栓塞与相关环境及器官对象之间的联系。对各特征的检验概率分析表明本文提取的特征大部分具有更好的真实栓塞识别能力。
⑤联合使用各类特征选择最优特征子集并优化分类器以改善肺栓塞计算机辅助检测系统的性能。首先提出一种基于蚁群算法的组合式特征选择算法,再选择分类器分别评估不同类别特征、特征选择子集、与人工筛选特征子集以筛选出分类性能最优的特征子集。在此基础上,进一步提出采用集成学习的方法改善分类器的性能。实验结果表明采用基于蚁群算法得到的特征子集,结合以随机森林为基分类器的集成学习算法能获得良好的分类性能。与其他肺栓塞CAD系统的性能比较说明本文所提出的CAD系统能够检测中心型、叶段、段以及亚段动脉中的栓塞,同时具有相对很低的假阳性率。
Featured by low rates of diagnosis and high motality rates, pulmonary embolism (PE) is one of the three most common cardiovascular diseases in Western countries. In China, it has long been mistakenly regarded as one of the rare diseases, but is gathering attention rapidly in recent years. Due to its lack of clinical specificities, PE could be easily neglected in clinical diagnosis. The mortality rate is high without early diagnosis and efficient treatment. The invention of computed tomography and angiography has made modern imaging examination a significant method for PE diagnosis. Amongst, CT pulmonary arteriography has been used as one of the main methods of clinical diagnosis. The assistance by computer-aided detection(CAD) system could further enhance accuracy and efficiency of PE diagnosis.
The research on PE CAD system incepted nearly ten years ago. However, it is still far from mature, with many issues remaining to be resolved in both theory and application. In China, the research is primitive at best. The current research, by reference to the normal procedures of PE CAD systems, proposes a new technical route for the designing of PE CAD systems. It takes into account central PE inside mediastinum as one of the key detection objects, and aims to reduce false positives in PE diagnosis. The tasks and innovations made at each step of the research are as follows:
Firstly, an automatic 3D lung segmentation algorithm, based on anatomy knowledge, is proposed with an aim to segment the left and right lungs. At first, pre-segmentation is made by thresholding method, after an analysis of the density distribution of tissues in CT images. With regards segmentation errors caused by bronchus, the top-to-down region growing method is proposed to extract airway lumen and to avoid leakage from airway to parenchyma, to dilate to include airway wall; then the methods of regional area analysis and area projection are proposed to judge and separate left and right lungs, then to repair the edges of parenchyma. Experimental results show the applicability of the algorithm to CT plain scan/enhancement scan data, the effectiveness in excluding airways between the two lungs and in the segmentation of complicated images.
Secondly, in order to provide a basis for segmentation of pulmonary artery inside mediastinum and diagnosis of central PE, the study, in an innovative fashion, adds the module of mediastinum segmentation to PE CAD system and proposes an automatic 3D mediastinum segmentation algorithm. As an anatomic region, the mediastinum does not have clear boundaries or shape. The study uses the idea of margin points in the description of the anatomical structure of 2D mediastinal area. The points are extracted by a method based on corner detection, and the paths between points by fast marching method. Meanwhile, the location information of context-contour is introduced in 2D segmentation process in order to limit and guarantee the continuity of the surface of 3D mediastinum. The segmentation result is evaluated by the cumulative distribution of deviation distance from the gold standard result. The experiment shows that the automatic segmentation algorithm based on context-contour mediastinum could achieve complete and smooth mediastinum, and the segmentation is accurate and efficient.
Thirdly, with an aim to detect PE in the pulmonary artery more accurately, the study proposes a segmentation plan from mediastinal to peripheral pulmonary artery, thus combining the tasks of traditional segmentation of pulmonary vascular and separation of artery and vein. With regards the problem of leakage and unconnected vessels in the tracing process, the following proposals are made: a vascular tree tracking method based on regional growing and slice marching is used for artery tracking; a characteristic pattern filtration method based on multiple-dimensioned second-order derivative is applied in order to eliminate or reduce the contact area of neighboring objects and to prevent leakage by combination with 3D morphological vessel section assessment technique; the unconnected vessels are repaired by extracting minimum cost path. Experimental results show the high sensitivity and robustness for segmentation of main pulmonary artery inside mediastinum. It is also proved that the proposed method could exclude most pulmonary veins effectively.
Forthly, with regards the medical characteristics of PE detection in CTPA images, the present research extracts different regions for candidate PE, based on existing research results, and proposes another eighteen features for describing the relationship between PE and related environment and organs, such as gray-level difference from surrounding environment, gray level of borders, partial morphological feature and feature based on pulmonary artery tree. The chi-square test shows that most of the proposed features have better detection capability for real PE.
Fifthly, the performance of the CAD system for PE diagnosis is improved by selecting an optimal subset among the proposed features and optimizing the classifier. The research proposes a hybrid feature selection algorithm based on ant colony algorithm. Then selected classifiers are used to evaluate features of different types, feature selection subsets and manually classified feature subsets, in order to acquire the optimal feature subset for classification. The performance of classifier is then improved by the method of ensemble learning. Experimental results show the satisfactory performance of the combination of the feature subset deriving from ant colony algorithm and the ensemble learning algorithm with random forest as the base classifier. A comparison of the performance of CAD systems for PE detection reveals that the proposed CAD system could detect central, segmental, section and sub-segmental embolism, and could reduce false positives.
引文
[1] M. Rodger and P. S. Wells. Diagnosis of pulmonary embolism[J]. Thrombosis Research, 2001, 103(6): V225-238.
[2]谷岩.肺栓塞的诊断和治疗进展[J].医学综述, 2009, 15(5): 714-717.
[3]施毅,陈正堂.现代呼吸病治疗学[M].北京:人民卫生出版社, 2002, 457.
[4] S. Z. Goldhaber. Recent advances in the diagnosis and lytic therapy of pulmonary embolism[J]. Chest, 1991, 99(4 Suppl): 173S-179S.
[5]季晓微.多层螺旋CT肺动脉造影在肺栓塞诊断中的应用价值[J].中国医学计算机成像杂志, 2010, 16(2): 171-174.
[6]罗华,梁瑛.肺血栓栓塞症的诊疗现状及展望[J].中华现代内科学杂志, 2008, 5(5): 411-415.
[7]张燕.多层螺旋CT肺动脉造影联合下肢静脉造影诊断肺栓塞和深静脉血栓的临床研究[D].博士,中国协和医科大学, 2004.
[8] P. Stein, C. Athanasoulis, A. Alavi, etc. Complications and validity of pulmonary angiography in acute pulmonary embolism[J]. Circulation, 1992, 85(2): 462-468.
[9] C. Bova, F. Greco, G. Misuraca, etc. Diagnostic utility of echocardiography in patients with suspected pulmonary embolism[J]. Am J Emerg Med, 2003, 21(3): 180-3.
[10] M. Remy-Jardin, J. Remy, L. Wattinne, etc. Central pulmonary thromboembolism: diagnosis with spiral volumetric CT with the single-breath-hold technique--comparison with pulmonary angiography[J]. Radiology, 1992, 185(2): 381-387.
[11] R. Pesavento, G. de Conti, I. Minotto, etc. The value of 64-detector row computed tomography for the exclusion of pulmonary embolism[J]. Thrombosis and Haemostasis, 2011, 105(5): 901-907.
[12] J. I. Jung, K. J. Kim, M. I. Ahn, etc. Detection of pulmonary embolism using 64-slice multidetector-row computed tomography: accuracy and reproducibility on different image reconstruction parameters[J]. Acta Radiologica, 2011, 52(4): 417-21.
[13] P. Cronin, J. G. Weg, E. A. Kazerooni. The role of multidetector computed tomography angiography for the diagnosis of pulmonary embolism[J]. Seminars in Nuclear Medicine, 2008, 38(6): 418-431.
[14] W. Wang, J. P. Zhou, L. Q. Wu, etc. False-positive computed tomography pulmonary angiography and ventilation-perfusion lung scan could mimic massive pulmonary embolism in patient with pulmonary vein stenosis after radiofrequency ablation[J].Respiratory Care, 2011.
[15] A. Torbicki, A. Perrier, S. Konstantinides, etc. Guidelines on the diagnosis and management of acute pulmonary embolism: the Task Force for the Diagnosis and Management of Acute Pulmonary Embolism of the European Society of Cardiology (ESC)[J]. Eur Heart J, 2008, 29(18): 2276-315.
[16] M. Remy-Jardin, M. Pistolesi, L. R. Goodman, etc. Management of suspected acute pulmonary embolism in the era of CT angiography: a statement from the Fleischner Society[J]. Radiology, 2007, 245(2): 315-329.
[17] B. Ghaye, D. Szapiro, I. Mastora, etc. Peripheral pulmonary arteries: how far in the lung does multi-detector row spiral CT allow analysis?[J]. vol. 219, ed: RSNA, 2001: 629-636.
[18] K. Marten, C. Engelke, M. Funke, etc. ECG-gated multislice spiral CT for diagnosis of acute pulmonary embolism[J]. Clin Radiol, 2003, 58(11): 862-868.
[19] S. D. Qanadli, M. El Hajjam, B. Mesurolle, etc. Pulmonary embolism detection: Prospective evaluation of dual-section helical CT versus selective pulmonary arteriography in 157 patients[J]. Radiology, 2000, 217(2): 447-455.
[20] I. Mastora, M. Remy-Jardin, P. Masson, etc. Severity of acute pulmonary embolism: evaluation of a new spiral CT angiographic score in correlation with echocardiographic data[J]. European Radiology, 2003, 13(1): 29-35.
[21] C. Engelke, E. J. Rummeny, K. Marten. Acute pulmonary embolism on MDCT of the chest: prediction of cor pulmonale and short-term patient survival from morphologic embolus burden[J]. AJR Am J Roentgenol, 2006, 186(5): 1265-1271.
[22] J. F. Meaney, J. G. Weg, T. L. Chenevert, etc. Diagnosis of pulmonary embolism with magnetic resonance angiography[J]. N Engl J Med, 1997, 336(20): 1422-1427.
[23] M. Oudkerk, E. J. van Beek, P. Wielopolski, etc. Comparison of contrast-enhanced magnetic resonance angiography and conventional pulmonary angiography for the diagnosis of pulmonary embolism: a prospective study[J]. Lancet, 2002, 359(9318): 1643-1647.
[24] A. Gupta, C. K. Frazer, J. M. Ferguson, etc. Acute pulmonary embolism: diagnosis with MR angiography[J]. Radiology, 1999, 210(2): 353-359.
[25] The PIOPED Investigators and P. Scan. Value of the ventilation/perfusion scan in acute pulmonary embolism: results of the prospective investigation of pulmonary embolism diagnosis (PIOPED)[J]. JAMA, 1990, 263(20): 2753-2759.
[26] P. A. Loud, D. S. Katz, D. A. Bruce, etc. Deep venous thrombosis with suspected pulmonary embolism: Detection with combined CT venography and pulmonaryangiography[J]. Radiology, 2001, 219(2): 498-502.
[27] K. Doi. Computer-aided diagnosis in medical imaging: Historical review, current status and future potential[J]. Computerized Medical Imaging and Graphics, 2007, 31(4-5): 198-211.
[28] J. P. Ko and D. P. Naidich. Computer-aided diagnosis and the evaluation of lung disease[J]. Journal of Thoracic Imaging, 2004, 19(3): 136-155.
[29] L. Zhao. Curvature lines for lesion detection and visualization in CT colonography[D]. PhD, Technische Universiteit Delft, Nederland, 2011.
[30] J. Dehmeshki, S. Halligan, S. Taylor, etc. Computer assisted detection software for CT colonography: effect of sphericity filter on performance characteristics for patients with and without fecal tagging[J]. European Radiology, 2007, 17(3): 662-668.
[31] U. J. Schoepf and P. Costello. CT angiography for diagnosis of pulmonary embolism: State of the Art1[J]. Radiology, 2004, 230(2): 329-337.
[32] H.-P. Chan, L. Hadjiiski, C. Zhou, etc. Computer-aided diagnosis of lung cancer and pulmonary embolism in computed tomography--A review[J]. Academic Radiology, 2008, 15(5): 535-555.
[33] Y. Masutani, H. MacMahon, K. Doi. Computerized detection of pulmonary embolism in spiral CT angiography based on volumetric image analysis[J]. IEEE Trans Med Imaging, 2002, 21(12): 1517-23.
[34]虞红伟.一种基于水平集的多尺度乳腺肿块分割方法[J].仪器仪表学报, 2010, 31(6).
[35]哈章.基于灰阶超声序列图像的乳腺肿瘤计算机辅助诊断[D].博士,中国科学技术大学, 2008.
[36]魏颖,徐心和,贾同,等.基于优化水平集方法的CT图像肺结节检测算法[J].系统仿真学报, 2006, 18(z2): 909-915.
[37]魏颖.基于多尺度形态学滤波的CT图像疑似肺结节提取[J].东北大学学报(自然科学版), 2008, 29(3) : 312-315.
[38]聂生东.基于CT图像的肺结节计算机辅助检测技术的研究进展[J].中国医学物理学杂志, 2009, 26(2): 1075-1079.
[39]李丽.基于CT影像分析的肺结节计算机辅助检测与诊断技术进展[J].国际生物医学工程杂志, 2009, 32(5): 283-286.
[40]贾同,赵大哲,杨金柱,等.一种基于HRCT影像的肺结节计算机辅助检测方法[J].系统仿真学报, 2008, 20(14): 3849-3852.
[41]贾同,魏颖,赵大哲.一种基于CT影像的肺癌病灶检测新方法[J].电子学报, 2010, 38(11): 2545-2549.
[42]何中市,王健,陈永峰,等.基于仿生模式识别的孤立性肺结节检测[J].广西师范大学学报(自然科学版), 2008, 26(2): 106-109.
[43]李野. CT结肠成像中应用计算机辅助检测对结肠息肉诊断价值探讨[J].中国实验诊断学, 2010, 14(1): 80-82.
[44] M. Das, A. Schneider, U. Schoepf, etc. Computer-aided diagnosis of peripheral pulmonary emboli[C]. Proc.RSNA, 2003: 351-352.
[45] S. Digumarthy, C. Kagay, A. Legasto, etc. Computer-aided detection (CAD) of acute pulmonary emboli: Evaluation in patients without significant pulmonary disease[C]. Proc.RSNA, 2006: 255.
[46] J. Jeudy, T. Flukinger, C. White. Evalution of pulmonary embolism using an automated computer-aided detection tool[C]. Proc. RSNA, 2006: 255.
[47] U. J. Schoepf, A. C. Schneider, M. Das, etc. Pulmonary embolism: computer-aided detection at multidetector row spiral computed tomography[J]. Journal of Thoracic Imaging, 2007, 22(4): 319-323.
[48] Z. V. Maizlin, P. M. Vos, M. C. Godoy, etc. Computer-aided detection of pulmonary embolism on CT angiography: initial experience[J]. Journal of Thoracic Imaging, 2007, 22(4): 324-329.
[49] M. Das, M. Salganicoff, A. Bakai, etc. Computer-aided detection of pulmonary embolism: Assessment of sensitivity with regard to vessel segments[J]. RSNA Program Book, 2006: 487.
[50] J. Liang and J. Bi. Computer aided detection of pulmonary embolism with tobogganing and mutiple instance classification in CT pulmonary angiography[J]. Inf Process Med Imaging, 2007, 20: 630-641.
[51] S. Buhmann, P. Herzog, J. Liang, etc. Clinical evaluation of a computer-aided diagnosis (CAD) prototype for the detection of pulmonary embolism[J]. Academic Radiology, 2007, 14(6): 651-658.
[52] K. Marten and C. Engelke. Computer-aided detection and automated CT volumetry of pulmonary nodules[J]. European Radiology, 2007, 17(4): 888-901.
[53] M. Das, G. Muhlenbruch, A. Helm, etc. Computer-aided detection of pulmonary embolism: influence on radiologists' detection performance with respect to vessel segments[J]. European Radiology, 2008, 18(7): 1350-1355.
[54] C. Zhou, H. Chan, S. Patel, etc. Preliminary Investigation of Computer-aided Detection of Pulmonary Embolism in Three-dimensional Computed Tomography Pulmonary Angiography Images1[J]. Academic Radiology, 2005, 12(6): 782-792.
[55] C. Zhou, H. Chan, L. Hadjiiski. Automated detection of pulmonary embolism(PE) in computed tomographic pulmonary angiographic(CTPA) images:multiscale hierarchical expectation-maximization segmentation of vessels and PEs.[C]. Proc.SPIE, 2007, 6514: 2F1-2F8.
[56] H. Boüma, J. J. Sonnemans, A. Vilanova, etc. Automatic Detection of Pulmonary Embolism in CTA Images[J]. IEEE Trans Med Imaging, 2009, 28(8): 1223-1230.
[57] C. Zhou. Computer-aided detection of pulmonary embolism in computed tomographic pulmonary angiography (CTPA): Performance evaluation with independent data sets[J]. Medical Physics, 2009, 36: 3385-3396.
[58] S. C. Park, B. E. Chapman, B. Zheng. A multi-stage approach to improve performance of computer-aided detection of pulmonary embolisms depicted on ct images: preliminary investigation[J]. Biomedical Engineering, IEEE Transactions on, 2010, PP(99): 1-1.
[59] C. Zhou, H. Chan, B. Sahiner, etc. Automatic multiscale enhancement and segmentation of pulmonary vessels in CT pulmonary angiography images for CAD applications[J]. Medical Physics, 2007, 34(12): 4567-4577.
[60] S. C. Park, B. Chapman, C. Deible, etc. Improving CAD performance in Pulmonary Embolism Detection: Preliminary Investigation[J]. Medical Imaging 2010: Computer - Aided Diagnosis, 2010, 7624: 1052.
[61] J. Peters, O. Ecabert, C. Meyer, etc. Automatic whole heart segmentation in static magnetic resonance image volumes[J]. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2007, 2007: 402-410.
[62] C. Kirbas and F. Quek. A review of vessel extraction techniques and algorithms[J]. ACM Computing Surveys, 2004, 36(2): 81-121.
[63] D. Lesage, E. Angelini, I. Bloch, etc. Design and study of flux-based features for 3D vascular tracking[C]. Proc.ISBI, 2009.
[64] W. J. Niessen, C. M. van Bemmel, A. E. Frangi, etc. Model-based segmentation of cardiac and vascular images[C]. Proc.ISBI, 2002: 22-25.
[65] S. W?rz and K. Rohr. 3D adaptive model-based segmentation of human vessels[C]. Proc. SPIE Internat. Symposium, 2007, 6511: Q5110-Q5110.
[66] Y. Yuan and A. C. S. Chung. Multi-scale Model-based Vessel Enhancement Using Local Line Integrals[C]. Proc.EMBS, 2008: 2225-2228.
[67] M. J. Chung, J. M. Goo, J. G. Im, etc. CT perfusion image of the lung - Value in the detection of pulmonary embolism in a porcine model[J]. Investigative Radiology, 2004, 39(10): 633-640.
[68] S. Ding, Y. Ye, J. Tu, etc. Region-based geometric modelling of human airways and arterial vessels[J]. Computerized Medical Imaging and Graphics, 2010, 34(2): 114-121.
[69] R. Sebbe, B. Gosselin, E. Coche, etc. Segmentation of opacified thorax vessels using model-driven active contour[C]. Proc.EMBS, 2005: 2535-2538.
[70] T. Bülow, R. Wiemker, T. Blaffert, etc. Automatic extraction of the pulmonary artery tree from multi-slice CT data[C]. Proc.SPIE, 2005, 5746: 730-740.
[71] T. Kitasaka, K. Mori, J. Hasegawa, etc. Automated extraction of aorta and pulmonary artery in mediastinum from 3D chest X-ray CT images without contrast medium[C]. Proc.SPIE, 2002, 4684: 1496-1506.
[72] R. Sebbe, B. Gosselin, E. Coche, etc. Pulmonary arteries segmentation and feature extraction through slice marching[J]. Proc.RISC workshop on Circuits, Systems and Signal Processing 2003.
[73] S. Tanaka and H. Hanaizumi. A Method for Separation of Pulmonary Artery from Vein Using Continuity of Lung Vessels[J]. IEIC Technical Report (Institute of Electronics, Information and Communication Engineers), 2006, 105(580): 101-104.
[74] M. G. Linguraru, J. A. Pura, R. L. Uitert, etc. Segmentation and quantification of pulmonary artery for noninvasive CT assessment of sickle cell secondary pulmonary hypertension[J]. Medical Physics, 2010, 37(4): 1522-1532.
[75] H. Lombaert, Y. Sun, L. Grady, etc. A multilevel banded graph cuts method for fast image segmentation[C]. Proc.ICCV, 2005, 1: 259-265.
[76] S. Nakamura, Y. Mekada, I. Ide, etc. Pulmonary artery and vein classification method using spatial arrangement features from X-ray CT image[J]. CARS 2005: Computer Assisted Radiology and Surgery, 2005, 1281: 1403-1450.
[77] D. J. Kroon. Available: http://www.mathworks.com/matlabcentral/fileexchange/21993-viewer3d
[78] Y. Masutani, H. Yoshida, P. M. MacEneaney, etc. Automated segmentation of colonic walls for computerized detection of polyps in CT colonography[J]. J Comput Assist Tomogr, 2001, 25(4): 629-638.
[79] C. Wang and Smedby. An Automatic Seeding Method For Coronary Artery Segmentation and Skeletonization in CTA[J]. The Insight Journal, 2008.
[80] B. Bouraoui, C. Ronse, J. Baruthio, etc. 3D segmentation of coronary arteries based on advanced mathematical morphology techniques[J]. Computerized Medical Imaging and Graphics, 2010, 34(5): 377-387.
[81] M. Hofer. CT teaching manual: a systematic approach to CT reading[M]. Thieme MedicalPublishers, 2007:15-17.
[82]贾同,孟琭,赵大哲,等.基于CT图像的自动肺实质分割方法[J].东北大学学报:自然科学版, 2008, 29(7): 965-967.
[83] S. Hu, E. Hoffman, J. Reinhardt. Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images[J]. IEEE Trans Med Imaging, 2001, 20(6): 490-498.
[84] X. Zhang, H. Wang, A. Smith, etc. Corner detection based on gradient correlation matrices of planar curves[J]. Pattern Recognition, 2010, 43(4): 1207-1223.
[85] L. Cohen and R. Kimmel. Global minimum for active contour models: A minimal path approach[J]. International Journal of Computer Vision, 1997, 24(1): 57-78.
[86] J. Sethian. Level set methods and fast marching methods[M]. Cambridge Monographs on Applied and Computational Mathematics Cambridge University Press, 1999.
[87] Q. Lin. Enhancement, Extraction, Visualization of 3D volume data[D]. Ph.D Dissertation, Linkoping University, Sweden, 2003.
[88] J. M. Flores and F. Schmitt. Segmentation, reconstruction and visualization of the pulmonary artery and the pulmonary vein from anatomical images of the visible human project[C]. Computer Science, 2005. ENC 2005. Sixth Mexican International Conference on, 2005: 136.
[89] C. Florin, N. Paragios, J. Williams. Globally optimal active contours, sequential Monte Carlo and on-line learning for vessel segmentation[J]. Computer Vision-ECCV 2006, 2006, 3953: 476-489.
[90] H. Zhang, Z. Bian, D. Jiang, etc. Level set method for pulmonary vessels extraction[C]. IEEE International Conference on Image Processing(ICIP), 2003: 1105-1108.
[91] A. Kiraly, E. Pichon, D. Naidich, etc. Analysis of arterial sub-trees affected by pulmonary emboli[C]. Proc.SPIE, 2004, 5370: 1720-1729.
[92] C. Wu, G. Agam, A. S. Roy, etc. Regulated morphology approach to fuzzy shape analysis with application to blood vessel extraction in thoracic CT scans[C]. Medical Imaging, Procs of SPIE, 2004, 5370: 1720-1729.
[93] A. Frangi, W. Niessen, K. Vincken, etc. Multiscale vessel enhancement filtering[J]. Lecture Notes in Computer Science, 1998: 130-137.
[94] Y. Sato, S. Nakajima, H. Atsumi, etc. 3D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images[J]. Lecture Notes in Computer Science, 1997: 213-222.
[95] H. Shikata, E. Hoffman, M. Sonka. Automated segmentation of pulmonary vascular tree from 3D CT images[C]. Proc.SPIE, 2004, 5369: 107-115.
[96] X. Zhou, T. Hayashi, T. Hara, etc. Automatic segmentation and recognition of anatomical lung structures from high-resolution chest CT images[J]. Computerized Medical Imaging and Graphics, 2006, 30(5): 299-313.
[97] J. N. Kaftan, A. Bakai, M. Das, etc. Locally adaptive fuzzy pulmonary vessel segmentation in contrast enhanced CT data[C]. Proceedings IEEE 5th International Symposium on Biomedical Imaging (ISBI), 2008: 101-104.
[98] C. Lorenz, I. Carlsen, T. Buzug, etc.Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images[M]. CVRMed-MRCAS'97, 1997, 233-242.
[99] L. M. J. Florack, B. M. ter Haar Romeny, J. J. Koenderink, etc. Scale and the differential structure of images[J]. Image and Vision Computing, 1992, 10(6): 376-388.
[100] T. Lindeberg. A scale selection principle for estimating image deformations[J]. Image and Vision Computing, 1998, 16(14): 961-977.
[101] W. Adler and B. Lausen. Bootstrap estimated true and false positive rates and ROC curve[J]. Computational Statistics & Data Analysis, 2009, 53(3): 718-729.
[102] T. Bülow, C. Lorenz, S. Renisch. A general framework for tree segmentation and reconstruction from medical volume data[M]. Medical Image Computing and Computer-Assisted Intervention– MICCAI 2004, 2004, 533-540.
[103] H. Yoshida, Y. Masutani, P. MacEneaney, etc. Computerized detection of colonic polyps at CT colonography on the basis of volumetric features: pilot study1[J]. Radiology, 2002, 222(2): 327.
[104] E. Pichon, C. Novak, A. Kiraly, etc. A novel method for pulmonary emboli visualization from high-resolution CT images[C]. Proc.SPIE, 2004, 5367: 161-170.
[105] H. Boüma. Vessel-diameter quantification and embolus detection in cta images[D]. Ph.D, Technische Universiteit, 2008.
[106] C. Zhou, H. Chan, B. Sahiner, etc. Computer-aided detection of pulmonary embolism in computed tomographic pulmonary angiography (CTPA): Performance evaluation with independent data sets[J]. Medical Physics, 2009, 36(8): 3385-3396.
[107] L. Van Vliet and P. Verbeek. Better geometric measurement based on photometric information[C]. Proc. IMTC, 1994: 1357-1357.
[108] P. Danielsson, Q. Lin, Q. Ye. Efficient detection of second-degree variations in 2D and 3D images[J]. Journal of Visual Communication and Image Representation, 2001, 12(3): 255-305.
[109] E. Yorn-Tov and G. Inbar. Selection of relevant features for classification of movementsfrom single movement-related potentials using a genetic algorithm[C]. 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001, 2: 1364-1366.
[110] L. Breiman. Bagging predictors[J]. Machine Learning, 1996, 24(2): 123-140.
[2]谷岩.肺栓塞的诊断和治疗进展[J].医学综述, 2009, 15(5): 714-717.
[3]施毅,陈正堂.现代呼吸病治疗学[M].北京:人民卫生出版社, 2002, 457.
[4] S. Z. Goldhaber. Recent advances in the diagnosis and lytic therapy of pulmonary embolism[J]. Chest, 1991, 99(4 Suppl): 173S-179S.
[5]季晓微.多层螺旋CT肺动脉造影在肺栓塞诊断中的应用价值[J].中国医学计算机成像杂志, 2010, 16(2): 171-174.
[6]罗华,梁瑛.肺血栓栓塞症的诊疗现状及展望[J].中华现代内科学杂志, 2008, 5(5): 411-415.
[7]张燕.多层螺旋CT肺动脉造影联合下肢静脉造影诊断肺栓塞和深静脉血栓的临床研究[D].博士,中国协和医科大学, 2004.
[8] P. Stein, C. Athanasoulis, A. Alavi, etc. Complications and validity of pulmonary angiography in acute pulmonary embolism[J]. Circulation, 1992, 85(2): 462-468.
[9] C. Bova, F. Greco, G. Misuraca, etc. Diagnostic utility of echocardiography in patients with suspected pulmonary embolism[J]. Am J Emerg Med, 2003, 21(3): 180-3.
[10] M. Remy-Jardin, J. Remy, L. Wattinne, etc. Central pulmonary thromboembolism: diagnosis with spiral volumetric CT with the single-breath-hold technique--comparison with pulmonary angiography[J]. Radiology, 1992, 185(2): 381-387.
[11] R. Pesavento, G. de Conti, I. Minotto, etc. The value of 64-detector row computed tomography for the exclusion of pulmonary embolism[J]. Thrombosis and Haemostasis, 2011, 105(5): 901-907.
[12] J. I. Jung, K. J. Kim, M. I. Ahn, etc. Detection of pulmonary embolism using 64-slice multidetector-row computed tomography: accuracy and reproducibility on different image reconstruction parameters[J]. Acta Radiologica, 2011, 52(4): 417-21.
[13] P. Cronin, J. G. Weg, E. A. Kazerooni. The role of multidetector computed tomography angiography for the diagnosis of pulmonary embolism[J]. Seminars in Nuclear Medicine, 2008, 38(6): 418-431.
[14] W. Wang, J. P. Zhou, L. Q. Wu, etc. False-positive computed tomography pulmonary angiography and ventilation-perfusion lung scan could mimic massive pulmonary embolism in patient with pulmonary vein stenosis after radiofrequency ablation[J].Respiratory Care, 2011.
[15] A. Torbicki, A. Perrier, S. Konstantinides, etc. Guidelines on the diagnosis and management of acute pulmonary embolism: the Task Force for the Diagnosis and Management of Acute Pulmonary Embolism of the European Society of Cardiology (ESC)[J]. Eur Heart J, 2008, 29(18): 2276-315.
[16] M. Remy-Jardin, M. Pistolesi, L. R. Goodman, etc. Management of suspected acute pulmonary embolism in the era of CT angiography: a statement from the Fleischner Society[J]. Radiology, 2007, 245(2): 315-329.
[17] B. Ghaye, D. Szapiro, I. Mastora, etc. Peripheral pulmonary arteries: how far in the lung does multi-detector row spiral CT allow analysis?[J]. vol. 219, ed: RSNA, 2001: 629-636.
[18] K. Marten, C. Engelke, M. Funke, etc. ECG-gated multislice spiral CT for diagnosis of acute pulmonary embolism[J]. Clin Radiol, 2003, 58(11): 862-868.
[19] S. D. Qanadli, M. El Hajjam, B. Mesurolle, etc. Pulmonary embolism detection: Prospective evaluation of dual-section helical CT versus selective pulmonary arteriography in 157 patients[J]. Radiology, 2000, 217(2): 447-455.
[20] I. Mastora, M. Remy-Jardin, P. Masson, etc. Severity of acute pulmonary embolism: evaluation of a new spiral CT angiographic score in correlation with echocardiographic data[J]. European Radiology, 2003, 13(1): 29-35.
[21] C. Engelke, E. J. Rummeny, K. Marten. Acute pulmonary embolism on MDCT of the chest: prediction of cor pulmonale and short-term patient survival from morphologic embolus burden[J]. AJR Am J Roentgenol, 2006, 186(5): 1265-1271.
[22] J. F. Meaney, J. G. Weg, T. L. Chenevert, etc. Diagnosis of pulmonary embolism with magnetic resonance angiography[J]. N Engl J Med, 1997, 336(20): 1422-1427.
[23] M. Oudkerk, E. J. van Beek, P. Wielopolski, etc. Comparison of contrast-enhanced magnetic resonance angiography and conventional pulmonary angiography for the diagnosis of pulmonary embolism: a prospective study[J]. Lancet, 2002, 359(9318): 1643-1647.
[24] A. Gupta, C. K. Frazer, J. M. Ferguson, etc. Acute pulmonary embolism: diagnosis with MR angiography[J]. Radiology, 1999, 210(2): 353-359.
[25] The PIOPED Investigators and P. Scan. Value of the ventilation/perfusion scan in acute pulmonary embolism: results of the prospective investigation of pulmonary embolism diagnosis (PIOPED)[J]. JAMA, 1990, 263(20): 2753-2759.
[26] P. A. Loud, D. S. Katz, D. A. Bruce, etc. Deep venous thrombosis with suspected pulmonary embolism: Detection with combined CT venography and pulmonaryangiography[J]. Radiology, 2001, 219(2): 498-502.
[27] K. Doi. Computer-aided diagnosis in medical imaging: Historical review, current status and future potential[J]. Computerized Medical Imaging and Graphics, 2007, 31(4-5): 198-211.
[28] J. P. Ko and D. P. Naidich. Computer-aided diagnosis and the evaluation of lung disease[J]. Journal of Thoracic Imaging, 2004, 19(3): 136-155.
[29] L. Zhao. Curvature lines for lesion detection and visualization in CT colonography[D]. PhD, Technische Universiteit Delft, Nederland, 2011.
[30] J. Dehmeshki, S. Halligan, S. Taylor, etc. Computer assisted detection software for CT colonography: effect of sphericity filter on performance characteristics for patients with and without fecal tagging[J]. European Radiology, 2007, 17(3): 662-668.
[31] U. J. Schoepf and P. Costello. CT angiography for diagnosis of pulmonary embolism: State of the Art1[J]. Radiology, 2004, 230(2): 329-337.
[32] H.-P. Chan, L. Hadjiiski, C. Zhou, etc. Computer-aided diagnosis of lung cancer and pulmonary embolism in computed tomography--A review[J]. Academic Radiology, 2008, 15(5): 535-555.
[33] Y. Masutani, H. MacMahon, K. Doi. Computerized detection of pulmonary embolism in spiral CT angiography based on volumetric image analysis[J]. IEEE Trans Med Imaging, 2002, 21(12): 1517-23.
[34]虞红伟.一种基于水平集的多尺度乳腺肿块分割方法[J].仪器仪表学报, 2010, 31(6).
[35]哈章.基于灰阶超声序列图像的乳腺肿瘤计算机辅助诊断[D].博士,中国科学技术大学, 2008.
[36]魏颖,徐心和,贾同,等.基于优化水平集方法的CT图像肺结节检测算法[J].系统仿真学报, 2006, 18(z2): 909-915.
[37]魏颖.基于多尺度形态学滤波的CT图像疑似肺结节提取[J].东北大学学报(自然科学版), 2008, 29(3) : 312-315.
[38]聂生东.基于CT图像的肺结节计算机辅助检测技术的研究进展[J].中国医学物理学杂志, 2009, 26(2): 1075-1079.
[39]李丽.基于CT影像分析的肺结节计算机辅助检测与诊断技术进展[J].国际生物医学工程杂志, 2009, 32(5): 283-286.
[40]贾同,赵大哲,杨金柱,等.一种基于HRCT影像的肺结节计算机辅助检测方法[J].系统仿真学报, 2008, 20(14): 3849-3852.
[41]贾同,魏颖,赵大哲.一种基于CT影像的肺癌病灶检测新方法[J].电子学报, 2010, 38(11): 2545-2549.
[42]何中市,王健,陈永峰,等.基于仿生模式识别的孤立性肺结节检测[J].广西师范大学学报(自然科学版), 2008, 26(2): 106-109.
[43]李野. CT结肠成像中应用计算机辅助检测对结肠息肉诊断价值探讨[J].中国实验诊断学, 2010, 14(1): 80-82.
[44] M. Das, A. Schneider, U. Schoepf, etc. Computer-aided diagnosis of peripheral pulmonary emboli[C]. Proc.RSNA, 2003: 351-352.
[45] S. Digumarthy, C. Kagay, A. Legasto, etc. Computer-aided detection (CAD) of acute pulmonary emboli: Evaluation in patients without significant pulmonary disease[C]. Proc.RSNA, 2006: 255.
[46] J. Jeudy, T. Flukinger, C. White. Evalution of pulmonary embolism using an automated computer-aided detection tool[C]. Proc. RSNA, 2006: 255.
[47] U. J. Schoepf, A. C. Schneider, M. Das, etc. Pulmonary embolism: computer-aided detection at multidetector row spiral computed tomography[J]. Journal of Thoracic Imaging, 2007, 22(4): 319-323.
[48] Z. V. Maizlin, P. M. Vos, M. C. Godoy, etc. Computer-aided detection of pulmonary embolism on CT angiography: initial experience[J]. Journal of Thoracic Imaging, 2007, 22(4): 324-329.
[49] M. Das, M. Salganicoff, A. Bakai, etc. Computer-aided detection of pulmonary embolism: Assessment of sensitivity with regard to vessel segments[J]. RSNA Program Book, 2006: 487.
[50] J. Liang and J. Bi. Computer aided detection of pulmonary embolism with tobogganing and mutiple instance classification in CT pulmonary angiography[J]. Inf Process Med Imaging, 2007, 20: 630-641.
[51] S. Buhmann, P. Herzog, J. Liang, etc. Clinical evaluation of a computer-aided diagnosis (CAD) prototype for the detection of pulmonary embolism[J]. Academic Radiology, 2007, 14(6): 651-658.
[52] K. Marten and C. Engelke. Computer-aided detection and automated CT volumetry of pulmonary nodules[J]. European Radiology, 2007, 17(4): 888-901.
[53] M. Das, G. Muhlenbruch, A. Helm, etc. Computer-aided detection of pulmonary embolism: influence on radiologists' detection performance with respect to vessel segments[J]. European Radiology, 2008, 18(7): 1350-1355.
[54] C. Zhou, H. Chan, S. Patel, etc. Preliminary Investigation of Computer-aided Detection of Pulmonary Embolism in Three-dimensional Computed Tomography Pulmonary Angiography Images1[J]. Academic Radiology, 2005, 12(6): 782-792.
[55] C. Zhou, H. Chan, L. Hadjiiski. Automated detection of pulmonary embolism(PE) in computed tomographic pulmonary angiographic(CTPA) images:multiscale hierarchical expectation-maximization segmentation of vessels and PEs.[C]. Proc.SPIE, 2007, 6514: 2F1-2F8.
[56] H. Boüma, J. J. Sonnemans, A. Vilanova, etc. Automatic Detection of Pulmonary Embolism in CTA Images[J]. IEEE Trans Med Imaging, 2009, 28(8): 1223-1230.
[57] C. Zhou. Computer-aided detection of pulmonary embolism in computed tomographic pulmonary angiography (CTPA): Performance evaluation with independent data sets[J]. Medical Physics, 2009, 36: 3385-3396.
[58] S. C. Park, B. E. Chapman, B. Zheng. A multi-stage approach to improve performance of computer-aided detection of pulmonary embolisms depicted on ct images: preliminary investigation[J]. Biomedical Engineering, IEEE Transactions on, 2010, PP(99): 1-1.
[59] C. Zhou, H. Chan, B. Sahiner, etc. Automatic multiscale enhancement and segmentation of pulmonary vessels in CT pulmonary angiography images for CAD applications[J]. Medical Physics, 2007, 34(12): 4567-4577.
[60] S. C. Park, B. Chapman, C. Deible, etc. Improving CAD performance in Pulmonary Embolism Detection: Preliminary Investigation[J]. Medical Imaging 2010: Computer - Aided Diagnosis, 2010, 7624: 1052.
[61] J. Peters, O. Ecabert, C. Meyer, etc. Automatic whole heart segmentation in static magnetic resonance image volumes[J]. Medical Image Computing and Computer-Assisted Intervention–MICCAI 2007, 2007: 402-410.
[62] C. Kirbas and F. Quek. A review of vessel extraction techniques and algorithms[J]. ACM Computing Surveys, 2004, 36(2): 81-121.
[63] D. Lesage, E. Angelini, I. Bloch, etc. Design and study of flux-based features for 3D vascular tracking[C]. Proc.ISBI, 2009.
[64] W. J. Niessen, C. M. van Bemmel, A. E. Frangi, etc. Model-based segmentation of cardiac and vascular images[C]. Proc.ISBI, 2002: 22-25.
[65] S. W?rz and K. Rohr. 3D adaptive model-based segmentation of human vessels[C]. Proc. SPIE Internat. Symposium, 2007, 6511: Q5110-Q5110.
[66] Y. Yuan and A. C. S. Chung. Multi-scale Model-based Vessel Enhancement Using Local Line Integrals[C]. Proc.EMBS, 2008: 2225-2228.
[67] M. J. Chung, J. M. Goo, J. G. Im, etc. CT perfusion image of the lung - Value in the detection of pulmonary embolism in a porcine model[J]. Investigative Radiology, 2004, 39(10): 633-640.
[68] S. Ding, Y. Ye, J. Tu, etc. Region-based geometric modelling of human airways and arterial vessels[J]. Computerized Medical Imaging and Graphics, 2010, 34(2): 114-121.
[69] R. Sebbe, B. Gosselin, E. Coche, etc. Segmentation of opacified thorax vessels using model-driven active contour[C]. Proc.EMBS, 2005: 2535-2538.
[70] T. Bülow, R. Wiemker, T. Blaffert, etc. Automatic extraction of the pulmonary artery tree from multi-slice CT data[C]. Proc.SPIE, 2005, 5746: 730-740.
[71] T. Kitasaka, K. Mori, J. Hasegawa, etc. Automated extraction of aorta and pulmonary artery in mediastinum from 3D chest X-ray CT images without contrast medium[C]. Proc.SPIE, 2002, 4684: 1496-1506.
[72] R. Sebbe, B. Gosselin, E. Coche, etc. Pulmonary arteries segmentation and feature extraction through slice marching[J]. Proc.RISC workshop on Circuits, Systems and Signal Processing 2003.
[73] S. Tanaka and H. Hanaizumi. A Method for Separation of Pulmonary Artery from Vein Using Continuity of Lung Vessels[J]. IEIC Technical Report (Institute of Electronics, Information and Communication Engineers), 2006, 105(580): 101-104.
[74] M. G. Linguraru, J. A. Pura, R. L. Uitert, etc. Segmentation and quantification of pulmonary artery for noninvasive CT assessment of sickle cell secondary pulmonary hypertension[J]. Medical Physics, 2010, 37(4): 1522-1532.
[75] H. Lombaert, Y. Sun, L. Grady, etc. A multilevel banded graph cuts method for fast image segmentation[C]. Proc.ICCV, 2005, 1: 259-265.
[76] S. Nakamura, Y. Mekada, I. Ide, etc. Pulmonary artery and vein classification method using spatial arrangement features from X-ray CT image[J]. CARS 2005: Computer Assisted Radiology and Surgery, 2005, 1281: 1403-1450.
[77] D. J. Kroon. Available: http://www.mathworks.com/matlabcentral/fileexchange/21993-viewer3d
[78] Y. Masutani, H. Yoshida, P. M. MacEneaney, etc. Automated segmentation of colonic walls for computerized detection of polyps in CT colonography[J]. J Comput Assist Tomogr, 2001, 25(4): 629-638.
[79] C. Wang and Smedby. An Automatic Seeding Method For Coronary Artery Segmentation and Skeletonization in CTA[J]. The Insight Journal, 2008.
[80] B. Bouraoui, C. Ronse, J. Baruthio, etc. 3D segmentation of coronary arteries based on advanced mathematical morphology techniques[J]. Computerized Medical Imaging and Graphics, 2010, 34(5): 377-387.
[81] M. Hofer. CT teaching manual: a systematic approach to CT reading[M]. Thieme MedicalPublishers, 2007:15-17.
[82]贾同,孟琭,赵大哲,等.基于CT图像的自动肺实质分割方法[J].东北大学学报:自然科学版, 2008, 29(7): 965-967.
[83] S. Hu, E. Hoffman, J. Reinhardt. Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images[J]. IEEE Trans Med Imaging, 2001, 20(6): 490-498.
[84] X. Zhang, H. Wang, A. Smith, etc. Corner detection based on gradient correlation matrices of planar curves[J]. Pattern Recognition, 2010, 43(4): 1207-1223.
[85] L. Cohen and R. Kimmel. Global minimum for active contour models: A minimal path approach[J]. International Journal of Computer Vision, 1997, 24(1): 57-78.
[86] J. Sethian. Level set methods and fast marching methods[M]. Cambridge Monographs on Applied and Computational Mathematics Cambridge University Press, 1999.
[87] Q. Lin. Enhancement, Extraction, Visualization of 3D volume data[D]. Ph.D Dissertation, Linkoping University, Sweden, 2003.
[88] J. M. Flores and F. Schmitt. Segmentation, reconstruction and visualization of the pulmonary artery and the pulmonary vein from anatomical images of the visible human project[C]. Computer Science, 2005. ENC 2005. Sixth Mexican International Conference on, 2005: 136.
[89] C. Florin, N. Paragios, J. Williams. Globally optimal active contours, sequential Monte Carlo and on-line learning for vessel segmentation[J]. Computer Vision-ECCV 2006, 2006, 3953: 476-489.
[90] H. Zhang, Z. Bian, D. Jiang, etc. Level set method for pulmonary vessels extraction[C]. IEEE International Conference on Image Processing(ICIP), 2003: 1105-1108.
[91] A. Kiraly, E. Pichon, D. Naidich, etc. Analysis of arterial sub-trees affected by pulmonary emboli[C]. Proc.SPIE, 2004, 5370: 1720-1729.
[92] C. Wu, G. Agam, A. S. Roy, etc. Regulated morphology approach to fuzzy shape analysis with application to blood vessel extraction in thoracic CT scans[C]. Medical Imaging, Procs of SPIE, 2004, 5370: 1720-1729.
[93] A. Frangi, W. Niessen, K. Vincken, etc. Multiscale vessel enhancement filtering[J]. Lecture Notes in Computer Science, 1998: 130-137.
[94] Y. Sato, S. Nakajima, H. Atsumi, etc. 3D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images[J]. Lecture Notes in Computer Science, 1997: 213-222.
[95] H. Shikata, E. Hoffman, M. Sonka. Automated segmentation of pulmonary vascular tree from 3D CT images[C]. Proc.SPIE, 2004, 5369: 107-115.
[96] X. Zhou, T. Hayashi, T. Hara, etc. Automatic segmentation and recognition of anatomical lung structures from high-resolution chest CT images[J]. Computerized Medical Imaging and Graphics, 2006, 30(5): 299-313.
[97] J. N. Kaftan, A. Bakai, M. Das, etc. Locally adaptive fuzzy pulmonary vessel segmentation in contrast enhanced CT data[C]. Proceedings IEEE 5th International Symposium on Biomedical Imaging (ISBI), 2008: 101-104.
[98] C. Lorenz, I. Carlsen, T. Buzug, etc.Multi-scale line segmentation with automatic estimation of width, contrast and tangential direction in 2D and 3D medical images[M]. CVRMed-MRCAS'97, 1997, 233-242.
[99] L. M. J. Florack, B. M. ter Haar Romeny, J. J. Koenderink, etc. Scale and the differential structure of images[J]. Image and Vision Computing, 1992, 10(6): 376-388.
[100] T. Lindeberg. A scale selection principle for estimating image deformations[J]. Image and Vision Computing, 1998, 16(14): 961-977.
[101] W. Adler and B. Lausen. Bootstrap estimated true and false positive rates and ROC curve[J]. Computational Statistics & Data Analysis, 2009, 53(3): 718-729.
[102] T. Bülow, C. Lorenz, S. Renisch. A general framework for tree segmentation and reconstruction from medical volume data[M]. Medical Image Computing and Computer-Assisted Intervention– MICCAI 2004, 2004, 533-540.
[103] H. Yoshida, Y. Masutani, P. MacEneaney, etc. Computerized detection of colonic polyps at CT colonography on the basis of volumetric features: pilot study1[J]. Radiology, 2002, 222(2): 327.
[104] E. Pichon, C. Novak, A. Kiraly, etc. A novel method for pulmonary emboli visualization from high-resolution CT images[C]. Proc.SPIE, 2004, 5367: 161-170.
[105] H. Boüma. Vessel-diameter quantification and embolus detection in cta images[D]. Ph.D, Technische Universiteit, 2008.
[106] C. Zhou, H. Chan, B. Sahiner, etc. Computer-aided detection of pulmonary embolism in computed tomographic pulmonary angiography (CTPA): Performance evaluation with independent data sets[J]. Medical Physics, 2009, 36(8): 3385-3396.
[107] L. Van Vliet and P. Verbeek. Better geometric measurement based on photometric information[C]. Proc. IMTC, 1994: 1357-1357.
[108] P. Danielsson, Q. Lin, Q. Ye. Efficient detection of second-degree variations in 2D and 3D images[J]. Journal of Visual Communication and Image Representation, 2001, 12(3): 255-305.
[109] E. Yorn-Tov and G. Inbar. Selection of relevant features for classification of movementsfrom single movement-related potentials using a genetic algorithm[C]. 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2001, 2: 1364-1366.
[110] L. Breiman. Bagging predictors[J]. Machine Learning, 1996, 24(2): 123-140.
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