面向肺疾病检测的胸腔CT影像分割研究
|
摘要
肺癌是当今世界人数死亡最多的癌症，在早期对它进行检测、诊断和治疗是提高肺癌患者生存率的重要手段。肺癌的早期征状是以小肺结节的形态出现，故对肺结节的及早检出与及时治疗对挽救肺癌患者生命具有重要意义。随着医学CT(Computed Tomography))技术的进步，医学CT检查获得影像数据的大量增加，其能提供更多器官、组织信息的同时，也给医生带来了极大的读片工作负担。为提高医生的工作效率、减轻工作强度和克服读片中人为因素的影响以及提高对肺疾病的检出率，急需研究对胸腔CT影像中各种肺疾病进行自动检测的方法。而研究这样的方法，其首要的任务就是对胸腔CT影像中肺各组织进行正确的分割。由此，本文以胸腔CT影像为研究对象，以肺疾病自动检测为目的，通过结合人体的肺解剖结构、CT成像机理等知识，对肺的各组织、病变的分割方法展开研究。研究大致内容如下：
从胸腔CT影像正确分割肺是进行肺疾病自动检测的重要步骤与首要任务，由此研究了一种结合同态学处理和CT阈值分割相结合的肺野分割算法。它在肺解剖知识模型的引导下，能够对有叶裂的肺野、分割粘连的肺野和有纵隔边缘凹陷的肺野均能进行正确处理和分割。
针对有高密度近胸膜肺结节肺野，研究了一种基于先验形状约束的活动轮廓模型的肺野分割的方法。首先它对已分割的肺野形状进行分类，并对这些形状进行分类学习获得其PCA(Principal ComponentAnalysis)形状模式向量；然后通过该先验向量模式与活动轮廓相结合的模型迭代拟合来完成对肺野的分割。研究表明采用该方法对较规范的有边缘高密度病变的肺野分割是完全可行的。
为更好地解决有高密度近胸膜肺结节肺野的分割问题，本文进一步研究利用相邻肺野的形状相似特征来正确分割该类肺野的方法。首先对胸腔CT影像中的肺野形状形成的相似流形和对肺野PCA流形进行了研究。然后研究通过肺野流形上点所表达的肺野关系采用流形插值重构肺野形状，再进行变形配准来减小误差的肺野分割方法。研究结果表明它是一种可行的分割方法，同时从分割结果的准确性和敏感性、特异性结果可看出它能分割除肺尖和肺底外有边缘病变肺野的正确区域。
对平扫CT、低剂量CT存在着的大量噪声的影像,开展了通过组合滤波、医学影像增强、分割和分数阶微分增强能力等方面的研究，最后研究出了先用分数阶微分算子对肺影像增强,再用局部最优阈值进行血管分割的肺血管分割方法。研究表明该方法可有效地提取血管网络并得到丰富的血管细节，对比传统肺血管分割方法可知它有更为准确的肺血管分割能力。
针对成像、重建噪声和部分容积效应的影响以及肺结节病变导致的胸腔CT影像中组织与肺结节病灶之间存在的边界模糊情况，研究了一种结合四邻域连接权的脉冲耦合神经网络结合先验形状能量函数的主动轮廓分割候选肺内结节和近胸膜肺结节的方法。研究表明它是一种切实可行且行之有效的候选肺结节分割方法。
在本文的最后部分，阐述了用胸腔CT影像对肺疾病进行检测的发展趋势，以及下一步将要开展的研究工作。然后对论文研究工作的主要内容、创新点进行了总结并对未来的研究工作做了进一步的展望。
As lung cancer is now the world's deadliest cancers, its early detection, diagnosis andtreatment are important means to improve the survival of patients with lung cancer. Theearly symptoms of the lung cancer are in the form of small pulmonary nodules, so theearly detection of lung nodules and treatment for it in time are significant to save thepatients. With the progress of medical CT (computed tomography) technology and theincrease of image data gotten in the medical CT examination, it provides moreinformation about organs and tissues, and at the same time gives a great burden to thedoctors. In order to improve the doctor's working efficiency, reduce the workingstrength, overcome the influence of artificial factors in the reading, and improve thedetection rate of lung disease, it is an urgent task to study the automatic detection oflung diseases. And for it, its primary task is to segment the organs or tissues in thethoracic CT scans correctly. Therefore, the dissertation takes the goal to automaticallydetect pulmonary diseases based on the thoracic CT scans, and studies the segmentationmethod of every lung tissues through the combination of the anatomical structure of thelungs and the knowledge of the CT imaging mechanism. It generally involves thefollowing contents:
As segmenting lung correctly from the thoracic CT scans is an important step and thefirst priority for the analysis, diagnosis and treatment of pulmonary diseases, it isproposed a segmentation algorithm which integrated with the homomorphismprocessing and the CT threshold segmentation. Under the guidance of lung anatomicknowledge model, it is able to segment the lung fields with lobar fissure, thin junctionbetween the lung fields, and indentation of the blood vessel and bronchi-wall in thehilus pulmonisvery well.
For the regular lung fields with the juxta-plural lung nodules of high density, it isproposed a method which an active contour model constrainted of the prior shape cansegment out the correct lung fields. It is to classify the lung field shape, decomposethese shapes into the PCA (Principal Component Analysis) shape vectors, and followedby the prior model vectors the improved active contour can fit the boundary and segments out the correct ones. Research results show it is completely feasible that themethod segments the lung fields whose edge affected by high density pathology.
In order to solve the segmentation problem better, the dissertation has studied anothersegmentation method for the lung fields by using the similar characteristics. To investthe shapes similarity manifold of the lung fields in a lung and the method to construct amanifold with the PCA, based on the manifold relationship among the lung fields, theaffected lung fields can be reconstructed by the manifold interpolating, and it registersto decrease the error and segments out the lung field. Results show that the method is aneffective method, and the sensitivity, the specificity and the accuracy illustrate that itcan segment out the correct ones except the apex and the bottom of lung.
Due to a lot of noise in the plain CT scans, low-dose CT scans, after a deepinvestigation into the composition filtering, medical image enhancement, imagesegmentation method and the enhancing ability to subtle details of the fractional orderdifferential, it is proposed a pulmonary blood vessels segmentation method. It segmentsevery local region of the thoracic CT scans enhanced by fractional differential operatorwith each optimal threshold. Studies have shown the method can effectively extract theblood vessels. Compared with the traditional pulmonary vascular segmentation ones, ithas more accurate segmentation ability of pulmonary vascular.
For the influences of the noises of the imaging, the reconstructing of the image andthe partial volume effects, and the pathological parts which lead to the border ofdifference tissures or organs blurry, it is proposed a segmentation method which iscombination of the four neighborhood connection power pulse coupled neural networkand an active contour model with the prior shape to extract the candidate lung nodules.Studies results show that the method is a feasible, and an effective one to segment thecandidate pulmonary nodule.
In the last part of the dissertation, it is elaborated the trends of detection for lungdisease in thoracic CT scans, as well as the next step to carry out research works. Thenits summarized the main content of the research work, the innovation points, anddiscuses further research problems in the future.
从胸腔CT影像正确分割肺是进行肺疾病自动检测的重要步骤与首要任务，由此研究了一种结合同态学处理和CT阈值分割相结合的肺野分割算法。它在肺解剖知识模型的引导下，能够对有叶裂的肺野、分割粘连的肺野和有纵隔边缘凹陷的肺野均能进行正确处理和分割。
针对有高密度近胸膜肺结节肺野，研究了一种基于先验形状约束的活动轮廓模型的肺野分割的方法。首先它对已分割的肺野形状进行分类，并对这些形状进行分类学习获得其PCA(Principal ComponentAnalysis)形状模式向量；然后通过该先验向量模式与活动轮廓相结合的模型迭代拟合来完成对肺野的分割。研究表明采用该方法对较规范的有边缘高密度病变的肺野分割是完全可行的。
为更好地解决有高密度近胸膜肺结节肺野的分割问题，本文进一步研究利用相邻肺野的形状相似特征来正确分割该类肺野的方法。首先对胸腔CT影像中的肺野形状形成的相似流形和对肺野PCA流形进行了研究。然后研究通过肺野流形上点所表达的肺野关系采用流形插值重构肺野形状，再进行变形配准来减小误差的肺野分割方法。研究结果表明它是一种可行的分割方法，同时从分割结果的准确性和敏感性、特异性结果可看出它能分割除肺尖和肺底外有边缘病变肺野的正确区域。
对平扫CT、低剂量CT存在着的大量噪声的影像,开展了通过组合滤波、医学影像增强、分割和分数阶微分增强能力等方面的研究，最后研究出了先用分数阶微分算子对肺影像增强,再用局部最优阈值进行血管分割的肺血管分割方法。研究表明该方法可有效地提取血管网络并得到丰富的血管细节，对比传统肺血管分割方法可知它有更为准确的肺血管分割能力。
针对成像、重建噪声和部分容积效应的影响以及肺结节病变导致的胸腔CT影像中组织与肺结节病灶之间存在的边界模糊情况，研究了一种结合四邻域连接权的脉冲耦合神经网络结合先验形状能量函数的主动轮廓分割候选肺内结节和近胸膜肺结节的方法。研究表明它是一种切实可行且行之有效的候选肺结节分割方法。
在本文的最后部分，阐述了用胸腔CT影像对肺疾病进行检测的发展趋势，以及下一步将要开展的研究工作。然后对论文研究工作的主要内容、创新点进行了总结并对未来的研究工作做了进一步的展望。
As lung cancer is now the world's deadliest cancers, its early detection, diagnosis andtreatment are important means to improve the survival of patients with lung cancer. Theearly symptoms of the lung cancer are in the form of small pulmonary nodules, so theearly detection of lung nodules and treatment for it in time are significant to save thepatients. With the progress of medical CT (computed tomography) technology and theincrease of image data gotten in the medical CT examination, it provides moreinformation about organs and tissues, and at the same time gives a great burden to thedoctors. In order to improve the doctor's working efficiency, reduce the workingstrength, overcome the influence of artificial factors in the reading, and improve thedetection rate of lung disease, it is an urgent task to study the automatic detection oflung diseases. And for it, its primary task is to segment the organs or tissues in thethoracic CT scans correctly. Therefore, the dissertation takes the goal to automaticallydetect pulmonary diseases based on the thoracic CT scans, and studies the segmentationmethod of every lung tissues through the combination of the anatomical structure of thelungs and the knowledge of the CT imaging mechanism. It generally involves thefollowing contents:
As segmenting lung correctly from the thoracic CT scans is an important step and thefirst priority for the analysis, diagnosis and treatment of pulmonary diseases, it isproposed a segmentation algorithm which integrated with the homomorphismprocessing and the CT threshold segmentation. Under the guidance of lung anatomicknowledge model, it is able to segment the lung fields with lobar fissure, thin junctionbetween the lung fields, and indentation of the blood vessel and bronchi-wall in thehilus pulmonisvery well.
For the regular lung fields with the juxta-plural lung nodules of high density, it isproposed a method which an active contour model constrainted of the prior shape cansegment out the correct lung fields. It is to classify the lung field shape, decomposethese shapes into the PCA (Principal Component Analysis) shape vectors, and followedby the prior model vectors the improved active contour can fit the boundary and segments out the correct ones. Research results show it is completely feasible that themethod segments the lung fields whose edge affected by high density pathology.
In order to solve the segmentation problem better, the dissertation has studied anothersegmentation method for the lung fields by using the similar characteristics. To investthe shapes similarity manifold of the lung fields in a lung and the method to construct amanifold with the PCA, based on the manifold relationship among the lung fields, theaffected lung fields can be reconstructed by the manifold interpolating, and it registersto decrease the error and segments out the lung field. Results show that the method is aneffective method, and the sensitivity, the specificity and the accuracy illustrate that itcan segment out the correct ones except the apex and the bottom of lung.
Due to a lot of noise in the plain CT scans, low-dose CT scans, after a deepinvestigation into the composition filtering, medical image enhancement, imagesegmentation method and the enhancing ability to subtle details of the fractional orderdifferential, it is proposed a pulmonary blood vessels segmentation method. It segmentsevery local region of the thoracic CT scans enhanced by fractional differential operatorwith each optimal threshold. Studies have shown the method can effectively extract theblood vessels. Compared with the traditional pulmonary vascular segmentation ones, ithas more accurate segmentation ability of pulmonary vascular.
For the influences of the noises of the imaging, the reconstructing of the image andthe partial volume effects, and the pathological parts which lead to the border ofdifference tissures or organs blurry, it is proposed a segmentation method which iscombination of the four neighborhood connection power pulse coupled neural networkand an active contour model with the prior shape to extract the candidate lung nodules.Studies results show that the method is a feasible, and an effective one to segment thecandidate pulmonary nodule.
In the last part of the dissertation, it is elaborated the trends of detection for lungdisease in thoracic CT scans, as well as the next step to carry out research works. Thenits summarized the main content of the research work, the innovation points, anddiscuses further research problems in the future.
引文
[1] A. Boutayeb, S. Boutayeb. The burden of non communicable diseases in developingcountries[J]. Int. J Equity Health,2005,4(1):2-9
[2] III S. Armato, M. Giger, H. MacMahon. Automated detection of lung nodules in CT scans:preliminary results[J]. Medical Physics,2001,28(8):1552-1561
[3] M. Puderbach, H. Kauczor. Can lung MR replace lung CT[J]. Pediatric Radiology,2008,38(3):439-451
[4] H. Abe, T. Ishida, J. Shiraishi, et al. Effect of temporal subtraction images on radiologists’detection of lung cancer on CT: results of the observer performance study with use of filmcomputed tomography images[J]. Academic Radiology,2004,11(12):1337-1343
[5] R. D. Pugatch, L. J. Faling. Computed tomography of thorax, a status report[J]. Chest,1981,80:618-626
[6] W.R. Webb. Advanced in computed tomography of the thorax[J]. Radiol. Clin. North A,21(4):723-739
[7] J. Dehmeshki, X.Ye, X. Lin, et al. Automated detection of lung nodules in CT images usingshape-based genetic algorithm[J]. Computerized Medical Imaging and Graphics,2007,31(6):408-417
[8] Y. Lee, T. Hara, H. Fujita, et al. Automated detection of pulmonary nodules in helical CTimages based on an improved template-matching technique[J]. IEEE Transactions on MedicalImaging,2001,20(7):595-604
[9] S. Matsumoto, H. Kundel, J. Gee, et al. Pulmonary nodule detection in CT images withquantized convergence index filter[J]. Medical Image Analysis,2006,10(3):343-352
[10] D. Paik, C. Beaulieu, G. Rubin, et al. Surface normal overlap: a computer-aided detectionalgorithm with application to colonic polyps and lung nodules in helical CT[J]. IEEETransactions on Medical Imaging,2004,23(6):661-675
[11] I. Sluimer, A. Schilham, M. Prokop, et al. Computer analysis of computed tomography scansof the lung: a survey[J]. IEEE Transactions on Medical Imaging,2006,25(4):385-405
[12]余建明等.医学影像技术学[M].北京:科学出版社,2004,174-395
[13]周燕发.胸部X线, CT, MRI诊断学[M].北京:科学技术出版社,2002,52-87
[14]彭裕文.人体解剖学[M].上海:复旦大学出版社，2005,12-124
[15] A. Pousse, L. Pastor, M. Parmentier, et al. Pulmonary nodule distribution modeling as adiagnostic tool for HRCT image analysis[J]. IEEE Transactions on Nuclear Science,2004,51(3):690-695
[16] A. P. Reeves, A. B. chan, D. F. Yankelevitz, et al. On measuring the change in size ofpulmonary nodules[J]. IEEE Transactions on Medical Imaging,2006,25(4):435-449
[17] S. G. Armato, M. L. Giger, C. J. Moran, et al. Computerized detection of pulmonary noduleson CT scans[J]. Radiographics,1999,19(5):1303–1311,
[18] G. Agam, S. G Armato, C. Wu. Vessel tree reconstruction in thoracic CT scans withapplication to nodule detection[J]. IEEE Transactions on Medical Imaging,2005,24(4):486-499
[19] S. G. Armato, W. F. Sensakovic. Automated lung segmentation for thoracic CT[J]. Acad.Radiol.,2004,11(9):1011-1021
[20] M. S. Brown, M. F. McNitt-Gray, N. J. Mankovich, et al. Method for segmenting chest CTimage data using an anatomical model: preliminary results[J]. IEEE Trans. Med. Imag.,1997,16(6):828-839
[21] S. Hu, E. A. Hoffman, J. M. Reinhardt. Automatic lung segmentation for accurate quantitationof volumetric X-ray CT images[J]. IEEE Trans. Med. Imag.,2001,20(6):490–498
[22] J. K. Leader, B. Zheng, R. M. Rogers, et al. Automated lung segmentation in X-ray computedtomography[J]. Acad. Radiol.,2003,10(11):1224–1236
[23] A. Frangi, W. Niessen, K. Vincken, et al. Multi-scale vessel enhancement filtering[C]. LectureNotes in Computer Science,1998,130-137
[24] Y. Sato, S. Nakajima, N. Shiraga, et al. Three-dimensional multi-scale line filter forsegmentation and visualization of curvilinear structures in medical images[J]. Med. ImageAnal.,1998,2(2):143–168
[25] H. Shikata, E. A. Hoffman, M. Sonka. Automated segmentation of pulmonary vascular treefrom3-D CT images[C]. Proc. SPIE,5369,2004:107–116
[26] C. Wu, G. Agam, A. S. Roy, et al. Armato. Regulated morphology approach to fuzzy shapeanalysis with application to blood vessel extraction in thoracic CT scans[C]. Proc. SPIE,5370,2004,1262–1270
[27] K. Hayashi, A. Aziz, K. Ashizawa, et al. Radiographic and CT appearances of the majorfissures[J]. Radiographics,,2001,21(4):861–874
[28] A. Yildiz, F. G lpinar, C. Mukadder, et al. HRCT evaluation of the accessory fissures of thelung[J]. Eur. J. Radiol.,2004(49):245–249
[29] M. Prokop, M. Galanski. Spiral and multislice computed tomography of the body[M].Stuttgart, Germany: Thieme,2003,
[30] K. Mori, J. Hasegawa, Y. Suenaga, et al. Automated anatomical labeling of the bronchialbranch and its application to the virtual bronchoscopy system[J]. IEEE Trans. Med. Imag.,2000,19(2):103–114
[31] A. P. Kiraly, W. E. Higgins, G. McLennan, et al. Three-dimensional human airwaysegmentation methods for clinical virtual bronchoscopy[J]. Acad. Radiol.,2002,9(10):1153–1168
[32] J. R. Ederle, C. P. Heussel, J. Hast, et al. Evaluation of changes in central airway dimensions,lung area and mean lung density at paired inspiratory/expiratory high-resolution computedtomography[J]. Eur. Radiol.,2003,13:2454–2461
[33] N. L. Müller, H. Coxson. Chronic obstructive pulmonary disease: imaging the lungs inpatients with chronic obstructive pulmonary disease[J]. Thorax,2002,57(11):982–985
[34] D. Aykac, E. A. Hoffman, G. McLennan, et al. Segmentation and analysis of the human airwaytree from three-dimensional X-ray CT images[J]. IEEE Trans. Med. Imag.,2003,22(8):940–950
[35] J. P. Ko, M. Betke. Chest CT: automated nodule detection and assessment of change overtime—preliminary experience[J]. Radiology,2001,218(1):267–273
[36] K. Awai, K. Murao, A. Ozawa, et al. Pulmonary nodules at chest CT: effect of computer-aideddiagnosis on radiologists’detection performance[J]. Radiology,2004,230(2):347–352
[37] M. N. Gurcan, B. Sahiner, N. Petrick, et al. Lung nodule detection on thoracic computedtomography images: preliminary evaluation of a computer-aided diagnosis system[J]. Med.Phys.,2002,29(11):2552–2558
[38] D. Wormanns, S. Diederich. Characterization of small pulmonary nodules by CT[J]. Eur.Radiol.,2004,14:1380–1391
[39] J. J. Erasmus, J. E. Connolly, H. P.McAdams, et al. Solitary pulmonary nodules: part I.Morphologic evaluation for differentiation of benign and malignant lesions[J]. Radiographics,2000,20(1):43–58
[40] J. P. Ko, D. P. Naidich. Lung nodule detection and characterization with multi-slice CT[J].Radiologic Clin. N. Am.,2003,41(3):575–597
[41] S. G. Armato, G. McLennan, M. F. McNitt-Gray,et al. Lung image database consortium:developing a resource for the medical imaging research community[J]. Radiology,2004,232(3):739–748
[42]郭光文，王序.人体解剖彩色图谱[M].北京：人民卫生出版社，2004，1-264
[43] Y. J.Zhang, J. J. Gerbrands. Transition region determination based thresholding[J]. PatternRecognition Letter,1991,12:13-23
[44] Y. Nakagawa, A. Rosenfeld. Some experiments on variable thresholding[J]. PatternRecognition,1979,11:191-204
[45] H. D. Li, Kallergi, L.P. Clarke, et al. Markov random field for tumor detection in digitalmammagraphy[J]. IEEE Trans. on Medical Imaging,1995,14:565-576
[46] C. Lee, S. Hun, T. A.Ketter, et al. Unsupervised connectivity-based thresholding segmentationof midsaggital brain MR images[J]. coput. Biol. Med.,1998,28:309-338
[47] A. Perez, R. C. Gonzalez. An iterative thresholding algorithm for image segmentation[J].IEEE Trans. on Pattern Analysis and Machine Intelligence,1987,9(6),742-751
[48] J. R. Parker. Gray Level thresholding in badly illuminated images[J]. IEEE Trans. on PatternAnalysis and Machine Intelligence,33(6),1991:875-1117
[49] J. K. Udupa, S. Samarasekera. Fuzzy connectedness and object definition: theory, algorithms,and applications in image segmentaion[J]. Grphical Model and Image Processing,1995,58(3):246-261
[50] I. N. Manousakas, P. E. Undrill, G. G. Cameron, et al. Split-and-merge segmentation ofmagnetic resonance medical images: performance evaluation and extension to threedimensions[J]. Computers and Biomedical Research,1998,31:393-412
[51] J. Besag. On the statistical analysis of dirty pictures[J]. J. R. Statist. Soc.,1996,48(3):259-302
[52] T. Lei and W. Sewchand. Statistical approach to X-Ray CT imaging and its applictions inimage analysis part2: a new stochastic model-based image segmentaion technique for X-RayCT image[J]. IEEE Trans. on Medical Imaging,1992,11(1):62-69
[53] Z. Liang, J. R. MacFall, D. P. Harrington. Parameter estimation and tissue segmentation frommultispectral MR images[J]. IEEE Trans. on Medical Imaging,1994,13:441-449
[54] J. C. Rajapakse, J. N. Giedd, J. L. Rapoport. Statistical approach to segmenttion ofsingle-channel cere-bral MR images[J]. IEEE Trans. on Medical Imaging,1997,16:176-186
[55] W. M. Wells, W. E. L. Grimson, R. Kikinis, et al. Adaptive segmentation of MRI data[J]. IEEETrans. On Medical Imaging,1996,15:429-442
[56] D. pham. J. Prince. Adadaptive fuzzy c-mean algorithm for image segmentation in thepresence of intensity inhomogeneities[J]. Pattern Recognition Letters,1999,30(1):57-68
[57] C. A. Murthy, S. K. Pal. Fuzzy thresholding: mathematical framework, bound functions andweighted moving average techniques[J]. Pattern Recognition Letter,1990,11:197-206
[58] J. K. Udupa, L. Wei, S. Samarasekera, et al. Multiole sclerosis lesion quatification usingfuzzy-connectedness principle[J]. IEEE Trans. on Medical Imaging,1997,16(5):598-609
[59] T. N. Papas. An adaptive clustering algorithm for image segmentation[J]. IEEE Trans. onSignal Process,1992,40:901-914
[60] S. Geman, D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restorationof images[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence,1984,6(6):721-741
[61] S. Lakshmanan, H. Derin. Simultaneous parameter estimation and segmentation of Gibbsrandom fields using simulated annealing[J]. IEEE Trans. on Pattern Analysisy and MachineInterlligence,1989,11(8):799-813
[62] D. Geman, S. Gean, C. Graffigne, et al. Boundary detection by constrained optimazation[J].IEEE Trans. on Patten Analysis and Machine Intelligence,1990,12(7):609-628
[63] V. S. Nalwa, T. O. Binford. On detecting edges[J]. IEEE Trans. on Pattern Analysis andMachine Intelligence,1986,8(6):699-711
[64] Robert M. Haralick. Digital step edges from zero crossing of second directional derivatives[J].IEEE Trans. on Pattern Analysis and Machine Intelligence,1984,6(1):58-68
[65] L. Matalas, R. Benjamin, R. Kitney. An edge detection technique using the facet model andparameterized relaxation labelling[J]. IEEE Trans. on Pattern Analysis and MachineIntelligence,1997,19(4):328-341
[66] L. H. Staib, J. S. Duncan. Boundary finding with parametrically deformable models[J]. IEEETrans. on Pattern Analysis and Machine Intelligence,1992,14(11):1061-1075
[67] A. Goshtasby. Design and recovery of2-d and3-d shapes using rational gaussian curves andsurfaces[J]. International Journal of Computer Vison,1993,10(3):233-256
[68] M. Wu, H. Sheu. Representation of3D surfaces by two-variable Fourier descriptors[J]. IEEETrans. on Pattern Analysis and Machine Intelligence,1998,20(8):858-863
[69] W. T. Freeman, E. H. Adelson. The design and use of steerable filters[J]. IEEE Trans. onPattern Analysis and Machine Intelligence,1991,13(9):891-906
[70] K. H. Liang, T. Tjahajadi, Y. H. Yang. Roof edge detection using regularized cubic B-splinefilting[J]. Pattern Recognition,1997,30(5):719-728
[71] J. Canny. A computational approach to edge detection[J]. IEEE Trans. on Pattern Analysis andMachine Intelligence,1986,8(6):679-698
[72] A. X. Falcao, J. K. Udupa, S. Samarasekera, et al. User-steered image segmentaionparadigms:live wire and live lane[J]. Graphic Models and Image Processing,1998,60:233-260
[73] N. J. Nilsson. Principles of artificial intelligence[M]. Berlin: Springer Verlag,1982
[74] A. Martelli. Edge detection using heuristic search methods[J]. Computer Graphics and ImageProcessing,1972,1:169-182
[75] R. Bellmann. Dynamic programming[M]. Princeton, NJ: Princeton University Press,1957
[76] R. Kimmel, A. Amir, A. M. Bruckstein. Finding shortest paths on surfaces using level setspropagation[J]. IEEE Trans. on Pattern Analysis and MachineIntelligence,1995,17(6):635-640
[77] S. Osher, J. A. Sethian. Fronts propagating with curvature-dependent speed: algorithms basedon Hamilto-Jacobi formulations[J]. Journal of Computational Physics,1988,79:12-49
[78] J. A. Sethian. Level set mehtods and fast marching methods: evolving interfaces incomputational geometry, fluid mechanics, computer vision, and material science[M].Cambridge, UK: Cambridge University Press,1999
[79] J. A. Sethian. An analysis of flame propagation[D]. Berkeley, CA:Dept. of Mathematics,University of California,1982
[80] L. Vincent, P. Soille. Wateresheds in digital spaces: an efficient algorithm based on immersionsimulations[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence,1991,13(6):583-598
[81] F. Meyer, S. Beucher. Morphological segmentaion[J]. Journal of visual communication andimage representaion,1990,1:21-46
[82] N. Giordana, W. Pieczynski. Estimation of generalized multisensor hidder Markov chaninsand unsupervised image segmentaion[J]. IEEE Trans. on Pattern Analysis and MachineIntelligence,1997,19(7):465-475
[83] B. Chanda, M. K. Kundu, Y. V. Padmaha. A multi-scale morphologic edge detector[J]. PatternRecognition,1998,31(10):1469-1478
[84] J. F. Haddon, J. F. Boyce. Image segmentation by unifying region and boundaryinformation[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence,1990,12(10):929-948
[85] S. A. Hojjatoleslami, J. Kittler. Region growing: A new approach[J]. IEEE Trans. ImageProcessing.1998,7(7):1079-1084
[86]田捷，包尚联，周明全.医学影像处理与分析[M].北京：电子工业出版社，2003，35-85
[87] M. Silveira, J. Marques. Automatic segmentation of the lungs using multiple active contoursand outlier model[C].28th Annual International Conference of the IEEE,2006,3122-3125
[88] Y. Zhu, Y. Tan, Y. Hua, et al. Automatic segmentation of ground-glass opacities in lung CTimages by using Markov random field-based algorithms[J]. J Digit Imaging,2012,25(3):409-422,
[89] P. Hua, Q. Song, M. Sonka, et al. Segmentation of pathological and diseased lung tissue in CTimages using a graph-search algorithm[C]. IEEE International Symposium on BiomedicalImaging,2011,2072-2075
[90] Y. Itai, H. Kim, S. Ishikawa. A segmentation method of lung areas by using snakes andautomatic detection of abnormal shadow on the areas[J]. International Journal of InnovativeComputing, Information and Control,2007,3(2):277-284
[91] M. Antonelli, B. Lazzerini, F. Marcelloni. Segmentation and reconstruction of the lung volumein CT images[C].2005ACM Symposium on Applied Computing,2005,255-259
[92] P. Korfiatis, S. Spyros, P. Sakellaropoulos, et al. Automated3D segmentation of lung fields inthin slice CT exploiting wavelet preprocessing[C]. CAIP2007, LNCS4673,2007,237–244
[93] S. G. Armato, M. L. Goger, K. Ashizawa, et al., Automated lung segmentation in digital lateralchest radiographs[J]. Medical Physics,1998,(24)8:1507-1520
[94] J. K. Leader, B. Zheng，R. M. Rogers，et a1．Automated lung segmentation in X-ray computedtomography: development and evalution of a heuristic threshold-based scheme[J]. Acadradiol，2003, l0(11):1224-1236．
[95] R. Shojaii, J. Alirezaie, P. Babyn. Automatic lung segmentation in CT images using watershedtransform[C]. ICIP2005, IEEE International Conference on Image Processing,2005,113-117.
[96]杨晓强，李斌，魏生民．基于解剖知识模型的医学图像分割方法研究们[J]．航天医学与医学工程,2005,18(1)：62-65．
[97] S. G. Armato, W. F. Sensakovic III. Automated lung segmentation for thoracic CT: impact oncomputer-aided diagnosis[J]. Acad Radiol,2004,11:1011-1021
[98] M. Kass, A. Witkin, D. Terzopoulos. Snakes: active contour models[J]. Int. J. Comp. Vis.,1987,1:321–331
[99] S. Ukil, J. M. Reinhardt. Smoothing lung segmentation surfaces in three-dimensional Xray CTimages using anatomic guidance[J]. Acad Radiol,2005,12:1502-1511
[100] A. El-Baz, G. Gimel Farb, R. Falk, et al. A framework for unsupervised segmentation of lungtissues from low dose computed tomography images[J]. Computer Vision approaches tomedical image analysis,2005,1270-1273
[101] J. Pu, J. Roos, C. Yi, et al. Adaptive border marching algorithm: automatic lung segmentationon chest CT images[J]. Comput Med Imaging Graph,2008,32(6):452-462
[102] K. Li, X. Wu, D. Z. Chen, et al. Optimal surface segmentation in volumetric images-agraph-theoretic approach[J]. PAMI,2006,28(1):119–134
[103] Q. Song, X. Wu, Y. Liu, et al. Simultaneous searching of globally optimal interacting surfaceswith convex shape prior penalty[C]. CVPR,2010,2879-2886
[104] E. M. van Rikxoort, B. de Hoop, M. A. Viergever, et al. Automatic lung segmentation fromthoracic CT scans using a hybrid approach with error detection[J]. Medical Physics,2009,36(7):2934-2947
[105] I. C. Sluimer, M. Niemeijer, B. V. Ginneken. Lung field segmentation from thin-slice CTscans in presence of severe pathology[C]. Proceedings of SPIE5370,2004,1447-1455
[106] M. N. Prasad, M. S. Brown, S. Ahmada, et al. Automatic segmentation of lung parenchyma inthe presence of diseases based on curvature of ribs[J]. Academic Radiology,2008,15(9):1173–1180
[107] A. El-Ba, G. Gimel farb, R. Falk, et al. A new stochastic framework for accurate lungsegmentation[J]. Med Image Comput Assist Interv.,2008,11(1):322-330
[108] J. Wang, F. Li, Q. Li. Automated segmentation of lungs with severe interstitial lung disease inCT[J]. Medical Physics,2009,36(10):4592–4599
[109] S. Haykin. Adaptive filter theory[M]. Upper Saddle River, N.J: Prentice Hall,1996
[110] I. Pitas, and Vanetsanopoulos. An nonlinear digital filters: principles and applications[M].Boston, Mass: Kluwer Academic Pubilshers
[111] N. Wiener. Extrapolation, interpolation and smoothing of stationary time series[M].Cambridge, MA: MIT Press,1942
[112] T. F. Chan, L. A. Vese. Active contours without edges[J]. IEEE transactions on ImageProcessing,2001,10(2):266-277
[113] V. Caselles, R. Kimmel, G. Sapiro. Geodesic active contours[J]. International Journal ofComputer Vision,1997,22(1):61–79
[114] T. W. Way, L. M. Hadjiiski, B. Sahiner, et al. Computer-aided diagnosis of pulmonary noduleson CT scans: segmentation and classification using3D active contours[J]. Med Phys,2006,33(7):2323–2337
[115] S. G. Armato, M. L. Giger, H. MacMahon. Automated lung segmentation in digitizedpostero-anterior chest radiographs[J]. Acad Radiol,1998,5:245-255
[116] P. Wang, W. G. O Dell. Auto-segmentation of juxta-pleural lung fields in chest CT using lungcontour corrected by anatomic landmarks［R］. Rochester, New York, American: University ofRochester Medical Center,2004
[117] B. Xavier, V. Pierre, T. Jean-philippe. A variational model for object segmentation usingboundary information and shape prior driven by the Mumford-Shah functional[J]. IJCV,2006,68(2):145-162
[118] M. P. D. Jolly, S. Lakshmanan, A. K. Jain. Vehicle segmentation and classification usingdeformable templates[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(3):293-308
[119] T. F. Cootes, C. J. Taylor, D. H. Cooper, et al. Active shape models-their training andapplication[J]. Computer Vision and Image Understanding,1995,61(1):38-59
[120] V. Caselles, F. Catte, T. Coll,et al. A geometric model for active contours in imageprocessing[J]. Numerische Mathematik,1993,66:1-31
[121] R. Malladi, J. A. Sethian, B. C. Vemuri. Shape modeling with front propagation: a level setapproach[J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1995,17:158-175
[122] T. McInerney, D. Terzopoulos. Deformable models in medical image analysis: a Survey[J].Medical Image Analysis,1996,1(2):91-108
[123] J. A. Sethian. Level set methods and fast marching methods[M]. Cambridge, UK: CambridgeUniv. Press,1999
[124] X. Han, C. Xu, J. L. Prince. A topology preserving geometric deformable model and itsapplication in brain cortical surface reconstruction, geometric level set methods in imaging,vision, and graphics[M], Osher S., and Paragios N.,eds. Springer Verlag,2003
[125] J. A. Sethian. A fast marching level set method for monotonically advancing fronts[C]. Proc.Nat’lAcademy of Sciences,1996,93,1591-1595
[126] D. Adalsteinsson, J.A.Sethian. The fast construction of extension velocities in level setmethods[J]. J.Computational Physics,1999,148:2-22
[127] R. Goldenberg, R. Kimmel, E. Rivlin, et al. Fast geodesic active contours[J]. IEEE Trans.Image Processing,2001,10(10):1467-1475
[128] D. L. Chop. Computing minimal surfaces via level set curvature flow[J]. J. ComputationalPhysics,1993,106(1):77-91
[129] D. Adalsteinsson, J. A. Sethian. A fast level set method for propagating interfaces[J].J.Computational Physics,1995,118:269-277
[130] H. K. Zhao, T. Chan, B. Merriman, et al. A variational level set approach to multiphasemotion[J]. J. Computational Physics,1996,127:179-195
[131] L. Vese, T. Chan. A multiphase level set framework for image processing using theMumford–Shah functional[J]. Int. J. of Computer Vision,2002,50(3):271-293
[132] A. Tsai, A. J. Yezzi, A. S. Willsky. Curve evolution implementation of the Mumford-Shahfunctional for image segmentation, denoising, interpolation, and magnification[J]. IEEETrans.on Image Processing,2001,10(8):1169–1186
[133] J. A. Sethian. Theory, algorithms,and applications of level set methods for propagatingsurfaces[J]. Acta Numerica,1996:309-395
[134] J. Montagnat, H. Delingette, N. Ayache. A review of deformable surfaces: topology,geometryand deformation[J]. Image and Vision Computing,2001,19:1023-1040
[135] C. Xu, D. L. Pham, J. L. Prince. Image segmentation using deformable models, in Handbookof Medical Imaging,2:Medical Image Processing and Analysis[M], NY, American: SPIE Press,2000,131-169
[136] Y. Chen, H.T agare, S. Thiruvenkadam, et al. Using shape priors in geometric active contoursin a variational framework[J]. Int. J. of Computer Vision,2002,50(3):315-328
[137] J. A. Sethian. Fast marching methods[J]. SIAM Review,1999,41:199-235
[138] J. Weickert, B. M. H. Romeny, M. A. Viergever. Efficient and reliable schemes for nonlineardiffusion filtering[J]. IEEE Transactions on image processing,1998,7(3):398-410
[139] D. Mumford, J. Shah. Boundary detection by minimizing functional[C]. Proc. IEEE Conf.Computer Vision and Pattern Recognition, San Francisco, CA,1985
[140] M. Leventon. Statistical models for medical image analysis[D]. Harvard, American: HarvardUniversity,2000,1-156
[141] A. Tsai, A. Yezzi, W. Wells, et al, A. Model-based curve evolution techniques for imagesegmentation[C]. In IEEE International Conference of Computer Vision and PatternRecognition,2001,463–468
[142] A. Tsai, A. Yezzi, W. Wells, et al. A shape-based approach to the segmentation of medicalimagery using level sets[J]. IEEE Transactions on Medical Imaging,2003,22(2):137–154
[143] A. Yezzi, A. Tsai, A. Willsky. A statistical approach to snakes for bimodal and trimodalimagery[C]. In International Conference of Computer Vision,1999,898–903
[144] Y. Chen, H.D. Tagare. Thiruvenkadam, S., Huang, F., Wilson, D., Gopinath, K.S., Briggsand,R.W., and Geiser, E.A. Using prior shapes in geometric active contours in a variationalframework[J]. International Journal of Computer Vision,2002,50(3):315–328
[145] X. Bresson, P. Vandergheynst, J. P. Thiran. A priori information in image segmentation: energyfunctional based on shape statistical model and image information[C]. In IEEE InternationalConference on Image Processing,2003,425–428
[146] D. Mumford, J. Shah. Optimal approximations of piecewise smooth functions and associatedvariational problems[J]. Communications on Pure and Applied Mathematics,1989,42:577–685
[147] C. C. McCulloch, R. A. Kaucic, P. R. S. Mendonca. Model-based detection of lung nodules incomputed tomography exams1-Thoracic computer-aided diagnosis[J]. Acad Radiol,2004,11(3):258-266
[148] J. Dehmeshiki, H. Amin, M. Valdivieso. Segmentation of pulmonary nodules in thoracic CTscans: a region growing approach[J]. IEEE T Med Imaging,2008,27(4):4-27
[149] S. Diciotti, G. Picozzi, M. Falchini.3-D acquiring algorithm of small lung nodules in spiralCT Images[J]. IEEE T Inf Technol B,2008,12(1):7-19
[150] E. Soleymanpour, H. R. Pourreza, E. Ansaripour. Fully Automatic lung segmentation and ribsuppression methods to improve nodule detection in chest radiographs[J]. Journal of MedicalSignals and Sensors,2011,1(3)：202-213
[151] A. Retico, P. Delogu, M. E. Fantacci. Lung nodule detection in low-dose and thin-slicecomputed tomography[J]. Comput Biol Med,2008,38(4):525-534
[152] Y. Yim, H. Hong. Correction of segmented lung boundary for inclusion of pleural nodules andpulmonary vessels in chest CT images[J]. Comput Biol Med,2008,38(8):845-857
[153] A. Riccardi, T. S. Petkov, G. Ferri. Computer-aided detection of lung nodules via3D fastradial transform, scale space representation, and Zernike MIP classification[J]. Med Phys,2011,38(4):1962-1971
[154] C. Zhang, J. Wang, N. Zhao. Reconstruction and analysis of multi-pose face images based onnonlinear dimensionality reduction[J]. Pattern Recogn,2004,37(7):325-336
[155] S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding.science,2000,290(22):2323-2326.
[156] J. B. Tenenbaum, V. D. Silva, J. C. Langford. A global geometric framework for nonlineardimensionality reduction. Sicence,2000,290(22):2319-2323.
[157] D. Hearn, et al. Computer graphics (Third Edition)[M]. BeiJing, China: Publishing house ofelectronics industry,2004
[158] C. Harris, M. Stephens. A combined corner and edge detector[C], Proceedings of Fourth AlveyVision Conference,1988,147-151
[159] O. S. Al-Kadi, D. Watson. Texture analysis of aggressive and nonaggressive lung tumor CECT images[J]. IEEE T Bio-Med Eng,2008,55(7):1822-1830
[160] A. R. Larici, M. L. Stortoand, M. Torge. Automated volumetry of pulmonary nodules onmultidetector CT influence of slice thickness, reconstruction algorithm and tube currentPreliminary results[J]. Radio med,2008,113(2):29-42
[161] X. Ye, X. Lin, J. Dehmeshki. Shape-based computer-aided detection of lung nodules inthoracic CT images[J]. IEEE T Bio-Med Eng,2009,56(7):1810-1820
[162] P. D. Korfiatis, A. N. Karahaliou, A. D. Kazantzi, Texture-based identification andcharacterization of interstitial pneumonia patterns[J]. IEEE T Inf Technol B,2010,14(3):675-680
[163] Y. Shang, R. Deklerck, E. Nyssen. Vascular active contour for vessel tree segmentation[J].IEEE T Bio-Med Eng,2011,58(4):1023-1032
[164] P. D. Korfiatis, C. Kalogeropoulou, A. N. Karahaliou. Vessel tree segmentation in presence ofinterstitial lung disease in MDCT[J]. IEEE T Inf Technol B,2011,15(2):214-220
[165] P. V. Lo, B. D. Ginneken, M. Bruijne. Vessel tree extraction using locally optimal paths[C].IEEE ISBI(2010),2010,680-683
[166] X. Zhu, X. Zhong, G. Xin, et. al. VOLES: vasularity-oriented level set algorithm forpulmonary vessel segmentaion in image guided intervention therapy[J]. IEEE ISBI2009
[167] S. R. Aylward, E. Bullitt. Initialization, noise, singularities, and scale in height ridge traversalfor tubular object centerline extraction[J]. IEEE T Med Imaging.2002,21(2):61-75
[168] S. You, E. Bas, D. Erdogmus. Extraction of samples from airway and vessel trees[C].33rdAnnual International Conference of the IEEE EMBS(2011), Boston, Massachusetts, USA,2011,5157-5160.
[169] P. K. Saha, Z. Gao, S. K. Alford. Topomorphologic separation of fused isointensity objects viamultiscale opening: separating arteries and veins in3-D pulmonary CT[J]. IEEE T MedImaging,2010,29(3):840-851
[170] P. Korfiatis, A. Karahaliou, L. Costaridou. Automated vessel tree segmentation: challenges incomputer aided quantification of diffuse parenchyma lung diseases[J]. IEEE ITAB (2009),Larnaca, Cyprus,2009
[171] Y. Pu, W. Wang, J. Zhou. Fractional differential approach to detecting textural features ofdigital image and its fractional differential filter implementation[J]. SCI CHINA SER F,2008,51(9):1319-1339
[172] Y. Pu, J. Zhou, X. Yuan. Fractional differential mask: a fractional differential-based approachfor multiscale texture enhancement[J]. IEEE T Image Process,2010,19(2):491-511.
[173] M. Sonka, V. Hlavac, R. Boyle. Image processing: analysis and machine vision (secondedition)[M]. BeiJing: Post&Telecom press,2003,87-88
[174] N. Otsu. A threshold selection method from gray-level histograms[J]. IEEE Transactions onSystems, Man and Cybernetics,1979,9(1):62-66
[175] Y. Zhang. Image engineering(ē)[M]. BeiJing: TSingHua press,2008,184-185
[176] S. NIE, L. LI, Z. CHEN. Pulmonary nodule segmentation algorithm based on three-domainmean shift clustering [J]. Journa1of East China Norma1University (Natura1Science),2008,1(1):60-67
[177] Y. Shi, F. Qi, Z. Xue, et al. Segmenting lung fields in serial chest radiographs using bothpopulation-based and patient-specific shape statistics [J]. IEEE transactions on MedicalImaging,2008,27(4):481-494
[178] P. Campadelli, E. Casiraghi, D. Artioli. A fully automated method for lung nodule detectionfrom postero-anterior chest radiographs [J]. IEEE transactions on Medical Imaging,2006,25(12):1588-1603
[179] I. Sluimer, M. Prokop, B. V. Ginneken. Toward automated segmentation of the pathologicallung in CT [J]. IEEE transactions on Medical Imaging,2005,24(8):1024-1038
[180] B. S. Zhao, G. Gamsu, M. S. Ginsburg, et al. Automatic detection of small lung nodules on CTutilizing a local density maximum algorithm[J]. J Appl Clin Med Phys,2003,4:248-260
[181] H. A. Bastawrous, T. Fukumoto, N. Nitta, et al. Detection of ground glass opacities in lung CTimages using gabor filters and neural networks[C]. Instrumentation and MeasurementTechnology Conference,2005
[182] Z. Y. Ge, B. Sahineer, H. P. Chan, et al. Computer-aided detection of lung nodules: falsepositive reduction using a3D gradient field method and3D ellipsoid fitting[J]. Med Phy,2005,32:2443-2454
[183] C. Bahlmann, X. L. Li, K. Okada. Local pulmonary structure classification for computer-aidednodule detection[C]. Proceeding of SPIE, Medical Imaging,2006,6144,1775-1785
[184] M. G. Penedo, M. J. Carreira, A. Mosquera, et al. Computer-aided diagnosis: a neural networkbased approach to lung nodule detection[J]. IEEE Trans on Medical Imaging,1998, l7(6):872-880
[185] K. T. Bae, J. S. Kim, Y. H. Na, et al. Pulmonary nodules：automated detection on CT imageswith morphologic matching algorithm-preliminary results[J]. Radiology,2005,236:286-294
[186] M. S. Brown, M. F. McNitt-Gary, J. G. Goldin, et al. Patient specific modules for lung noduledetection and surveillance in CT images[J]. IEEE Trans on Medical Imaging,2001,20:1242-1250
[187] G. Kuntimad, H. S. Ranganath. Perfect image segmentation using pulse coupled neuralnetworks [J]. IEEE transactions on Neural Network,1999,10(3):591-598
[188]彭真明,蒋彪,肖峻等.基于并行点火PCNN模型的图像分割新方法[J].自动化学报,2008,34(9):1169-1173
[189] Q. Li，F. Li，K. Doi．Computerized detection of lung nodules in thin-section CT images by useof selective enhancement filters and an automated rule-based classifier[J]. Acad Radiol,2008,15:165-175
[190]张娟.基于Hessian矩阵的肺结节计算机辅助检测算法研究[D].山东泰州：泰山医学院，2009，15-25
[191]李彬，欧陕兴，田联房等.肺结节计算机辅助检测与定位系统[J].计算机应用研究，2010,27(6):2184-2188
[192] J. Hanley, B. McNeil. The meaning and use of the area under a receiver operatingcharacteristic (ROC) curve[J]. Radiology,1982,143(1):29-36
[193] C. Metz. Basic principles of ROC analysis[J]. Seminars in Nuclear Medicine,1978,8(4):283-298
[194] W. Kostis, A. Reeves, D. Yankelevitz, et al. Three-dimensional segmentation and growth-rateestimation of small pulmonary nodules in helical CT images[J]. IEEE Transactions onMedical Imaging,2003,22(10):1259-1274
[2] III S. Armato, M. Giger, H. MacMahon. Automated detection of lung nodules in CT scans:preliminary results[J]. Medical Physics,2001,28(8):1552-1561
[3] M. Puderbach, H. Kauczor. Can lung MR replace lung CT[J]. Pediatric Radiology,2008,38(3):439-451
[4] H. Abe, T. Ishida, J. Shiraishi, et al. Effect of temporal subtraction images on radiologists’detection of lung cancer on CT: results of the observer performance study with use of filmcomputed tomography images[J]. Academic Radiology,2004,11(12):1337-1343
[5] R. D. Pugatch, L. J. Faling. Computed tomography of thorax, a status report[J]. Chest,1981,80:618-626
[6] W.R. Webb. Advanced in computed tomography of the thorax[J]. Radiol. Clin. North A,21(4):723-739
[7] J. Dehmeshki, X.Ye, X. Lin, et al. Automated detection of lung nodules in CT images usingshape-based genetic algorithm[J]. Computerized Medical Imaging and Graphics,2007,31(6):408-417
[8] Y. Lee, T. Hara, H. Fujita, et al. Automated detection of pulmonary nodules in helical CTimages based on an improved template-matching technique[J]. IEEE Transactions on MedicalImaging,2001,20(7):595-604
[9] S. Matsumoto, H. Kundel, J. Gee, et al. Pulmonary nodule detection in CT images withquantized convergence index filter[J]. Medical Image Analysis,2006,10(3):343-352
[10] D. Paik, C. Beaulieu, G. Rubin, et al. Surface normal overlap: a computer-aided detectionalgorithm with application to colonic polyps and lung nodules in helical CT[J]. IEEETransactions on Medical Imaging,2004,23(6):661-675
[11] I. Sluimer, A. Schilham, M. Prokop, et al. Computer analysis of computed tomography scansof the lung: a survey[J]. IEEE Transactions on Medical Imaging,2006,25(4):385-405
[12]余建明等.医学影像技术学[M].北京:科学出版社,2004,174-395
[13]周燕发.胸部X线, CT, MRI诊断学[M].北京:科学技术出版社,2002,52-87
[14]彭裕文.人体解剖学[M].上海:复旦大学出版社，2005,12-124
[15] A. Pousse, L. Pastor, M. Parmentier, et al. Pulmonary nodule distribution modeling as adiagnostic tool for HRCT image analysis[J]. IEEE Transactions on Nuclear Science,2004,51(3):690-695
[16] A. P. Reeves, A. B. chan, D. F. Yankelevitz, et al. On measuring the change in size ofpulmonary nodules[J]. IEEE Transactions on Medical Imaging,2006,25(4):435-449
[17] S. G. Armato, M. L. Giger, C. J. Moran, et al. Computerized detection of pulmonary noduleson CT scans[J]. Radiographics,1999,19(5):1303–1311,
[18] G. Agam, S. G Armato, C. Wu. Vessel tree reconstruction in thoracic CT scans withapplication to nodule detection[J]. IEEE Transactions on Medical Imaging,2005,24(4):486-499
[19] S. G. Armato, W. F. Sensakovic. Automated lung segmentation for thoracic CT[J]. Acad.Radiol.,2004,11(9):1011-1021
[20] M. S. Brown, M. F. McNitt-Gray, N. J. Mankovich, et al. Method for segmenting chest CTimage data using an anatomical model: preliminary results[J]. IEEE Trans. Med. Imag.,1997,16(6):828-839
[21] S. Hu, E. A. Hoffman, J. M. Reinhardt. Automatic lung segmentation for accurate quantitationof volumetric X-ray CT images[J]. IEEE Trans. Med. Imag.,2001,20(6):490–498
[22] J. K. Leader, B. Zheng, R. M. Rogers, et al. Automated lung segmentation in X-ray computedtomography[J]. Acad. Radiol.,2003,10(11):1224–1236
[23] A. Frangi, W. Niessen, K. Vincken, et al. Multi-scale vessel enhancement filtering[C]. LectureNotes in Computer Science,1998,130-137
[24] Y. Sato, S. Nakajima, N. Shiraga, et al. Three-dimensional multi-scale line filter forsegmentation and visualization of curvilinear structures in medical images[J]. Med. ImageAnal.,1998,2(2):143–168
[25] H. Shikata, E. A. Hoffman, M. Sonka. Automated segmentation of pulmonary vascular treefrom3-D CT images[C]. Proc. SPIE,5369,2004:107–116
[26] C. Wu, G. Agam, A. S. Roy, et al. Armato. Regulated morphology approach to fuzzy shapeanalysis with application to blood vessel extraction in thoracic CT scans[C]. Proc. SPIE,5370,2004,1262–1270
[27] K. Hayashi, A. Aziz, K. Ashizawa, et al. Radiographic and CT appearances of the majorfissures[J]. Radiographics,,2001,21(4):861–874
[28] A. Yildiz, F. G lpinar, C. Mukadder, et al. HRCT evaluation of the accessory fissures of thelung[J]. Eur. J. Radiol.,2004(49):245–249
[29] M. Prokop, M. Galanski. Spiral and multislice computed tomography of the body[M].Stuttgart, Germany: Thieme,2003,
[30] K. Mori, J. Hasegawa, Y. Suenaga, et al. Automated anatomical labeling of the bronchialbranch and its application to the virtual bronchoscopy system[J]. IEEE Trans. Med. Imag.,2000,19(2):103–114
[31] A. P. Kiraly, W. E. Higgins, G. McLennan, et al. Three-dimensional human airwaysegmentation methods for clinical virtual bronchoscopy[J]. Acad. Radiol.,2002,9(10):1153–1168
[32] J. R. Ederle, C. P. Heussel, J. Hast, et al. Evaluation of changes in central airway dimensions,lung area and mean lung density at paired inspiratory/expiratory high-resolution computedtomography[J]. Eur. Radiol.,2003,13:2454–2461
[33] N. L. Müller, H. Coxson. Chronic obstructive pulmonary disease: imaging the lungs inpatients with chronic obstructive pulmonary disease[J]. Thorax,2002,57(11):982–985
[34] D. Aykac, E. A. Hoffman, G. McLennan, et al. Segmentation and analysis of the human airwaytree from three-dimensional X-ray CT images[J]. IEEE Trans. Med. Imag.,2003,22(8):940–950
[35] J. P. Ko, M. Betke. Chest CT: automated nodule detection and assessment of change overtime—preliminary experience[J]. Radiology,2001,218(1):267–273
[36] K. Awai, K. Murao, A. Ozawa, et al. Pulmonary nodules at chest CT: effect of computer-aideddiagnosis on radiologists’detection performance[J]. Radiology,2004,230(2):347–352
[37] M. N. Gurcan, B. Sahiner, N. Petrick, et al. Lung nodule detection on thoracic computedtomography images: preliminary evaluation of a computer-aided diagnosis system[J]. Med.Phys.,2002,29(11):2552–2558
[38] D. Wormanns, S. Diederich. Characterization of small pulmonary nodules by CT[J]. Eur.Radiol.,2004,14:1380–1391
[39] J. J. Erasmus, J. E. Connolly, H. P.McAdams, et al. Solitary pulmonary nodules: part I.Morphologic evaluation for differentiation of benign and malignant lesions[J]. Radiographics,2000,20(1):43–58
[40] J. P. Ko, D. P. Naidich. Lung nodule detection and characterization with multi-slice CT[J].Radiologic Clin. N. Am.,2003,41(3):575–597
[41] S. G. Armato, G. McLennan, M. F. McNitt-Gray,et al. Lung image database consortium:developing a resource for the medical imaging research community[J]. Radiology,2004,232(3):739–748
[42]郭光文，王序.人体解剖彩色图谱[M].北京：人民卫生出版社，2004，1-264
[43] Y. J.Zhang, J. J. Gerbrands. Transition region determination based thresholding[J]. PatternRecognition Letter,1991,12:13-23
[44] Y. Nakagawa, A. Rosenfeld. Some experiments on variable thresholding[J]. PatternRecognition,1979,11:191-204
[45] H. D. Li, Kallergi, L.P. Clarke, et al. Markov random field for tumor detection in digitalmammagraphy[J]. IEEE Trans. on Medical Imaging,1995,14:565-576
[46] C. Lee, S. Hun, T. A.Ketter, et al. Unsupervised connectivity-based thresholding segmentationof midsaggital brain MR images[J]. coput. Biol. Med.,1998,28:309-338
[47] A. Perez, R. C. Gonzalez. An iterative thresholding algorithm for image segmentation[J].IEEE Trans. on Pattern Analysis and Machine Intelligence,1987,9(6),742-751
[48] J. R. Parker. Gray Level thresholding in badly illuminated images[J]. IEEE Trans. on PatternAnalysis and Machine Intelligence,33(6),1991:875-1117
[49] J. K. Udupa, S. Samarasekera. Fuzzy connectedness and object definition: theory, algorithms,and applications in image segmentaion[J]. Grphical Model and Image Processing,1995,58(3):246-261
[50] I. N. Manousakas, P. E. Undrill, G. G. Cameron, et al. Split-and-merge segmentation ofmagnetic resonance medical images: performance evaluation and extension to threedimensions[J]. Computers and Biomedical Research,1998,31:393-412
[51] J. Besag. On the statistical analysis of dirty pictures[J]. J. R. Statist. Soc.,1996,48(3):259-302
[52] T. Lei and W. Sewchand. Statistical approach to X-Ray CT imaging and its applictions inimage analysis part2: a new stochastic model-based image segmentaion technique for X-RayCT image[J]. IEEE Trans. on Medical Imaging,1992,11(1):62-69
[53] Z. Liang, J. R. MacFall, D. P. Harrington. Parameter estimation and tissue segmentation frommultispectral MR images[J]. IEEE Trans. on Medical Imaging,1994,13:441-449
[54] J. C. Rajapakse, J. N. Giedd, J. L. Rapoport. Statistical approach to segmenttion ofsingle-channel cere-bral MR images[J]. IEEE Trans. on Medical Imaging,1997,16:176-186
[55] W. M. Wells, W. E. L. Grimson, R. Kikinis, et al. Adaptive segmentation of MRI data[J]. IEEETrans. On Medical Imaging,1996,15:429-442
[56] D. pham. J. Prince. Adadaptive fuzzy c-mean algorithm for image segmentation in thepresence of intensity inhomogeneities[J]. Pattern Recognition Letters,1999,30(1):57-68
[57] C. A. Murthy, S. K. Pal. Fuzzy thresholding: mathematical framework, bound functions andweighted moving average techniques[J]. Pattern Recognition Letter,1990,11:197-206
[58] J. K. Udupa, L. Wei, S. Samarasekera, et al. Multiole sclerosis lesion quatification usingfuzzy-connectedness principle[J]. IEEE Trans. on Medical Imaging,1997,16(5):598-609
[59] T. N. Papas. An adaptive clustering algorithm for image segmentation[J]. IEEE Trans. onSignal Process,1992,40:901-914
[60] S. Geman, D. Geman. Stochastic relaxation, Gibbs distributions, and the Bayesian restorationof images[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence,1984,6(6):721-741
[61] S. Lakshmanan, H. Derin. Simultaneous parameter estimation and segmentation of Gibbsrandom fields using simulated annealing[J]. IEEE Trans. on Pattern Analysisy and MachineInterlligence,1989,11(8):799-813
[62] D. Geman, S. Gean, C. Graffigne, et al. Boundary detection by constrained optimazation[J].IEEE Trans. on Patten Analysis and Machine Intelligence,1990,12(7):609-628
[63] V. S. Nalwa, T. O. Binford. On detecting edges[J]. IEEE Trans. on Pattern Analysis andMachine Intelligence,1986,8(6):699-711
[64] Robert M. Haralick. Digital step edges from zero crossing of second directional derivatives[J].IEEE Trans. on Pattern Analysis and Machine Intelligence,1984,6(1):58-68
[65] L. Matalas, R. Benjamin, R. Kitney. An edge detection technique using the facet model andparameterized relaxation labelling[J]. IEEE Trans. on Pattern Analysis and MachineIntelligence,1997,19(4):328-341
[66] L. H. Staib, J. S. Duncan. Boundary finding with parametrically deformable models[J]. IEEETrans. on Pattern Analysis and Machine Intelligence,1992,14(11):1061-1075
[67] A. Goshtasby. Design and recovery of2-d and3-d shapes using rational gaussian curves andsurfaces[J]. International Journal of Computer Vison,1993,10(3):233-256
[68] M. Wu, H. Sheu. Representation of3D surfaces by two-variable Fourier descriptors[J]. IEEETrans. on Pattern Analysis and Machine Intelligence,1998,20(8):858-863
[69] W. T. Freeman, E. H. Adelson. The design and use of steerable filters[J]. IEEE Trans. onPattern Analysis and Machine Intelligence,1991,13(9):891-906
[70] K. H. Liang, T. Tjahajadi, Y. H. Yang. Roof edge detection using regularized cubic B-splinefilting[J]. Pattern Recognition,1997,30(5):719-728
[71] J. Canny. A computational approach to edge detection[J]. IEEE Trans. on Pattern Analysis andMachine Intelligence,1986,8(6):679-698
[72] A. X. Falcao, J. K. Udupa, S. Samarasekera, et al. User-steered image segmentaionparadigms:live wire and live lane[J]. Graphic Models and Image Processing,1998,60:233-260
[73] N. J. Nilsson. Principles of artificial intelligence[M]. Berlin: Springer Verlag,1982
[74] A. Martelli. Edge detection using heuristic search methods[J]. Computer Graphics and ImageProcessing,1972,1:169-182
[75] R. Bellmann. Dynamic programming[M]. Princeton, NJ: Princeton University Press,1957
[76] R. Kimmel, A. Amir, A. M. Bruckstein. Finding shortest paths on surfaces using level setspropagation[J]. IEEE Trans. on Pattern Analysis and MachineIntelligence,1995,17(6):635-640
[77] S. Osher, J. A. Sethian. Fronts propagating with curvature-dependent speed: algorithms basedon Hamilto-Jacobi formulations[J]. Journal of Computational Physics,1988,79:12-49
[78] J. A. Sethian. Level set mehtods and fast marching methods: evolving interfaces incomputational geometry, fluid mechanics, computer vision, and material science[M].Cambridge, UK: Cambridge University Press,1999
[79] J. A. Sethian. An analysis of flame propagation[D]. Berkeley, CA:Dept. of Mathematics,University of California,1982
[80] L. Vincent, P. Soille. Wateresheds in digital spaces: an efficient algorithm based on immersionsimulations[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence,1991,13(6):583-598
[81] F. Meyer, S. Beucher. Morphological segmentaion[J]. Journal of visual communication andimage representaion,1990,1:21-46
[82] N. Giordana, W. Pieczynski. Estimation of generalized multisensor hidder Markov chaninsand unsupervised image segmentaion[J]. IEEE Trans. on Pattern Analysis and MachineIntelligence,1997,19(7):465-475
[83] B. Chanda, M. K. Kundu, Y. V. Padmaha. A multi-scale morphologic edge detector[J]. PatternRecognition,1998,31(10):1469-1478
[84] J. F. Haddon, J. F. Boyce. Image segmentation by unifying region and boundaryinformation[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence,1990,12(10):929-948
[85] S. A. Hojjatoleslami, J. Kittler. Region growing: A new approach[J]. IEEE Trans. ImageProcessing.1998,7(7):1079-1084
[86]田捷，包尚联，周明全.医学影像处理与分析[M].北京：电子工业出版社，2003，35-85
[87] M. Silveira, J. Marques. Automatic segmentation of the lungs using multiple active contoursand outlier model[C].28th Annual International Conference of the IEEE,2006,3122-3125
[88] Y. Zhu, Y. Tan, Y. Hua, et al. Automatic segmentation of ground-glass opacities in lung CTimages by using Markov random field-based algorithms[J]. J Digit Imaging,2012,25(3):409-422,
[89] P. Hua, Q. Song, M. Sonka, et al. Segmentation of pathological and diseased lung tissue in CTimages using a graph-search algorithm[C]. IEEE International Symposium on BiomedicalImaging,2011,2072-2075
[90] Y. Itai, H. Kim, S. Ishikawa. A segmentation method of lung areas by using snakes andautomatic detection of abnormal shadow on the areas[J]. International Journal of InnovativeComputing, Information and Control,2007,3(2):277-284
[91] M. Antonelli, B. Lazzerini, F. Marcelloni. Segmentation and reconstruction of the lung volumein CT images[C].2005ACM Symposium on Applied Computing,2005,255-259
[92] P. Korfiatis, S. Spyros, P. Sakellaropoulos, et al. Automated3D segmentation of lung fields inthin slice CT exploiting wavelet preprocessing[C]. CAIP2007, LNCS4673,2007,237–244
[93] S. G. Armato, M. L. Goger, K. Ashizawa, et al., Automated lung segmentation in digital lateralchest radiographs[J]. Medical Physics,1998,(24)8:1507-1520
[94] J. K. Leader, B. Zheng，R. M. Rogers，et a1．Automated lung segmentation in X-ray computedtomography: development and evalution of a heuristic threshold-based scheme[J]. Acadradiol，2003, l0(11):1224-1236．
[95] R. Shojaii, J. Alirezaie, P. Babyn. Automatic lung segmentation in CT images using watershedtransform[C]. ICIP2005, IEEE International Conference on Image Processing,2005,113-117.
[96]杨晓强，李斌，魏生民．基于解剖知识模型的医学图像分割方法研究们[J]．航天医学与医学工程,2005,18(1)：62-65．
[97] S. G. Armato, W. F. Sensakovic III. Automated lung segmentation for thoracic CT: impact oncomputer-aided diagnosis[J]. Acad Radiol,2004,11:1011-1021
[98] M. Kass, A. Witkin, D. Terzopoulos. Snakes: active contour models[J]. Int. J. Comp. Vis.,1987,1:321–331
[99] S. Ukil, J. M. Reinhardt. Smoothing lung segmentation surfaces in three-dimensional Xray CTimages using anatomic guidance[J]. Acad Radiol,2005,12:1502-1511
[100] A. El-Baz, G. Gimel Farb, R. Falk, et al. A framework for unsupervised segmentation of lungtissues from low dose computed tomography images[J]. Computer Vision approaches tomedical image analysis,2005,1270-1273
[101] J. Pu, J. Roos, C. Yi, et al. Adaptive border marching algorithm: automatic lung segmentationon chest CT images[J]. Comput Med Imaging Graph,2008,32(6):452-462
[102] K. Li, X. Wu, D. Z. Chen, et al. Optimal surface segmentation in volumetric images-agraph-theoretic approach[J]. PAMI,2006,28(1):119–134
[103] Q. Song, X. Wu, Y. Liu, et al. Simultaneous searching of globally optimal interacting surfaceswith convex shape prior penalty[C]. CVPR,2010,2879-2886
[104] E. M. van Rikxoort, B. de Hoop, M. A. Viergever, et al. Automatic lung segmentation fromthoracic CT scans using a hybrid approach with error detection[J]. Medical Physics,2009,36(7):2934-2947
[105] I. C. Sluimer, M. Niemeijer, B. V. Ginneken. Lung field segmentation from thin-slice CTscans in presence of severe pathology[C]. Proceedings of SPIE5370,2004,1447-1455
[106] M. N. Prasad, M. S. Brown, S. Ahmada, et al. Automatic segmentation of lung parenchyma inthe presence of diseases based on curvature of ribs[J]. Academic Radiology,2008,15(9):1173–1180
[107] A. El-Ba, G. Gimel farb, R. Falk, et al. A new stochastic framework for accurate lungsegmentation[J]. Med Image Comput Assist Interv.,2008,11(1):322-330
[108] J. Wang, F. Li, Q. Li. Automated segmentation of lungs with severe interstitial lung disease inCT[J]. Medical Physics,2009,36(10):4592–4599
[109] S. Haykin. Adaptive filter theory[M]. Upper Saddle River, N.J: Prentice Hall,1996
[110] I. Pitas, and Vanetsanopoulos. An nonlinear digital filters: principles and applications[M].Boston, Mass: Kluwer Academic Pubilshers
[111] N. Wiener. Extrapolation, interpolation and smoothing of stationary time series[M].Cambridge, MA: MIT Press,1942
[112] T. F. Chan, L. A. Vese. Active contours without edges[J]. IEEE transactions on ImageProcessing,2001,10(2):266-277
[113] V. Caselles, R. Kimmel, G. Sapiro. Geodesic active contours[J]. International Journal ofComputer Vision,1997,22(1):61–79
[114] T. W. Way, L. M. Hadjiiski, B. Sahiner, et al. Computer-aided diagnosis of pulmonary noduleson CT scans: segmentation and classification using3D active contours[J]. Med Phys,2006,33(7):2323–2337
[115] S. G. Armato, M. L. Giger, H. MacMahon. Automated lung segmentation in digitizedpostero-anterior chest radiographs[J]. Acad Radiol,1998,5:245-255
[116] P. Wang, W. G. O Dell. Auto-segmentation of juxta-pleural lung fields in chest CT using lungcontour corrected by anatomic landmarks［R］. Rochester, New York, American: University ofRochester Medical Center,2004
[117] B. Xavier, V. Pierre, T. Jean-philippe. A variational model for object segmentation usingboundary information and shape prior driven by the Mumford-Shah functional[J]. IJCV,2006,68(2):145-162
[118] M. P. D. Jolly, S. Lakshmanan, A. K. Jain. Vehicle segmentation and classification usingdeformable templates[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,1996,18(3):293-308
[119] T. F. Cootes, C. J. Taylor, D. H. Cooper, et al. Active shape models-their training andapplication[J]. Computer Vision and Image Understanding,1995,61(1):38-59
[120] V. Caselles, F. Catte, T. Coll,et al. A geometric model for active contours in imageprocessing[J]. Numerische Mathematik,1993,66:1-31
[121] R. Malladi, J. A. Sethian, B. C. Vemuri. Shape modeling with front propagation: a level setapproach[J]. IEEE Trans. Pattern Analysis and Machine Intelligence,1995,17:158-175
[122] T. McInerney, D. Terzopoulos. Deformable models in medical image analysis: a Survey[J].Medical Image Analysis,1996,1(2):91-108
[123] J. A. Sethian. Level set methods and fast marching methods[M]. Cambridge, UK: CambridgeUniv. Press,1999
[124] X. Han, C. Xu, J. L. Prince. A topology preserving geometric deformable model and itsapplication in brain cortical surface reconstruction, geometric level set methods in imaging,vision, and graphics[M], Osher S., and Paragios N.,eds. Springer Verlag,2003
[125] J. A. Sethian. A fast marching level set method for monotonically advancing fronts[C]. Proc.Nat’lAcademy of Sciences,1996,93,1591-1595
[126] D. Adalsteinsson, J.A.Sethian. The fast construction of extension velocities in level setmethods[J]. J.Computational Physics,1999,148:2-22
[127] R. Goldenberg, R. Kimmel, E. Rivlin, et al. Fast geodesic active contours[J]. IEEE Trans.Image Processing,2001,10(10):1467-1475
[128] D. L. Chop. Computing minimal surfaces via level set curvature flow[J]. J. ComputationalPhysics,1993,106(1):77-91
[129] D. Adalsteinsson, J. A. Sethian. A fast level set method for propagating interfaces[J].J.Computational Physics,1995,118:269-277
[130] H. K. Zhao, T. Chan, B. Merriman, et al. A variational level set approach to multiphasemotion[J]. J. Computational Physics,1996,127:179-195
[131] L. Vese, T. Chan. A multiphase level set framework for image processing using theMumford–Shah functional[J]. Int. J. of Computer Vision,2002,50(3):271-293
[132] A. Tsai, A. J. Yezzi, A. S. Willsky. Curve evolution implementation of the Mumford-Shahfunctional for image segmentation, denoising, interpolation, and magnification[J]. IEEETrans.on Image Processing,2001,10(8):1169–1186
[133] J. A. Sethian. Theory, algorithms,and applications of level set methods for propagatingsurfaces[J]. Acta Numerica,1996:309-395
[134] J. Montagnat, H. Delingette, N. Ayache. A review of deformable surfaces: topology,geometryand deformation[J]. Image and Vision Computing,2001,19:1023-1040
[135] C. Xu, D. L. Pham, J. L. Prince. Image segmentation using deformable models, in Handbookof Medical Imaging,2:Medical Image Processing and Analysis[M], NY, American: SPIE Press,2000,131-169
[136] Y. Chen, H.T agare, S. Thiruvenkadam, et al. Using shape priors in geometric active contoursin a variational framework[J]. Int. J. of Computer Vision,2002,50(3):315-328
[137] J. A. Sethian. Fast marching methods[J]. SIAM Review,1999,41:199-235
[138] J. Weickert, B. M. H. Romeny, M. A. Viergever. Efficient and reliable schemes for nonlineardiffusion filtering[J]. IEEE Transactions on image processing,1998,7(3):398-410
[139] D. Mumford, J. Shah. Boundary detection by minimizing functional[C]. Proc. IEEE Conf.Computer Vision and Pattern Recognition, San Francisco, CA,1985
[140] M. Leventon. Statistical models for medical image analysis[D]. Harvard, American: HarvardUniversity,2000,1-156
[141] A. Tsai, A. Yezzi, W. Wells, et al, A. Model-based curve evolution techniques for imagesegmentation[C]. In IEEE International Conference of Computer Vision and PatternRecognition,2001,463–468
[142] A. Tsai, A. Yezzi, W. Wells, et al. A shape-based approach to the segmentation of medicalimagery using level sets[J]. IEEE Transactions on Medical Imaging,2003,22(2):137–154
[143] A. Yezzi, A. Tsai, A. Willsky. A statistical approach to snakes for bimodal and trimodalimagery[C]. In International Conference of Computer Vision,1999,898–903
[144] Y. Chen, H.D. Tagare. Thiruvenkadam, S., Huang, F., Wilson, D., Gopinath, K.S., Briggsand,R.W., and Geiser, E.A. Using prior shapes in geometric active contours in a variationalframework[J]. International Journal of Computer Vision,2002,50(3):315–328
[145] X. Bresson, P. Vandergheynst, J. P. Thiran. A priori information in image segmentation: energyfunctional based on shape statistical model and image information[C]. In IEEE InternationalConference on Image Processing,2003,425–428
[146] D. Mumford, J. Shah. Optimal approximations of piecewise smooth functions and associatedvariational problems[J]. Communications on Pure and Applied Mathematics,1989,42:577–685
[147] C. C. McCulloch, R. A. Kaucic, P. R. S. Mendonca. Model-based detection of lung nodules incomputed tomography exams1-Thoracic computer-aided diagnosis[J]. Acad Radiol,2004,11(3):258-266
[148] J. Dehmeshiki, H. Amin, M. Valdivieso. Segmentation of pulmonary nodules in thoracic CTscans: a region growing approach[J]. IEEE T Med Imaging,2008,27(4):4-27
[149] S. Diciotti, G. Picozzi, M. Falchini.3-D acquiring algorithm of small lung nodules in spiralCT Images[J]. IEEE T Inf Technol B,2008,12(1):7-19
[150] E. Soleymanpour, H. R. Pourreza, E. Ansaripour. Fully Automatic lung segmentation and ribsuppression methods to improve nodule detection in chest radiographs[J]. Journal of MedicalSignals and Sensors,2011,1(3)：202-213
[151] A. Retico, P. Delogu, M. E. Fantacci. Lung nodule detection in low-dose and thin-slicecomputed tomography[J]. Comput Biol Med,2008,38(4):525-534
[152] Y. Yim, H. Hong. Correction of segmented lung boundary for inclusion of pleural nodules andpulmonary vessels in chest CT images[J]. Comput Biol Med,2008,38(8):845-857
[153] A. Riccardi, T. S. Petkov, G. Ferri. Computer-aided detection of lung nodules via3D fastradial transform, scale space representation, and Zernike MIP classification[J]. Med Phys,2011,38(4):1962-1971
[154] C. Zhang, J. Wang, N. Zhao. Reconstruction and analysis of multi-pose face images based onnonlinear dimensionality reduction[J]. Pattern Recogn,2004,37(7):325-336
[155] S. T. Roweis and L. K. Saul. Nonlinear dimensionality reduction by locally linear embedding.science,2000,290(22):2323-2326.
[156] J. B. Tenenbaum, V. D. Silva, J. C. Langford. A global geometric framework for nonlineardimensionality reduction. Sicence,2000,290(22):2319-2323.
[157] D. Hearn, et al. Computer graphics (Third Edition)[M]. BeiJing, China: Publishing house ofelectronics industry,2004
[158] C. Harris, M. Stephens. A combined corner and edge detector[C], Proceedings of Fourth AlveyVision Conference,1988,147-151
[159] O. S. Al-Kadi, D. Watson. Texture analysis of aggressive and nonaggressive lung tumor CECT images[J]. IEEE T Bio-Med Eng,2008,55(7):1822-1830
[160] A. R. Larici, M. L. Stortoand, M. Torge. Automated volumetry of pulmonary nodules onmultidetector CT influence of slice thickness, reconstruction algorithm and tube currentPreliminary results[J]. Radio med,2008,113(2):29-42
[161] X. Ye, X. Lin, J. Dehmeshki. Shape-based computer-aided detection of lung nodules inthoracic CT images[J]. IEEE T Bio-Med Eng,2009,56(7):1810-1820
[162] P. D. Korfiatis, A. N. Karahaliou, A. D. Kazantzi, Texture-based identification andcharacterization of interstitial pneumonia patterns[J]. IEEE T Inf Technol B,2010,14(3):675-680
[163] Y. Shang, R. Deklerck, E. Nyssen. Vascular active contour for vessel tree segmentation[J].IEEE T Bio-Med Eng,2011,58(4):1023-1032
[164] P. D. Korfiatis, C. Kalogeropoulou, A. N. Karahaliou. Vessel tree segmentation in presence ofinterstitial lung disease in MDCT[J]. IEEE T Inf Technol B,2011,15(2):214-220
[165] P. V. Lo, B. D. Ginneken, M. Bruijne. Vessel tree extraction using locally optimal paths[C].IEEE ISBI(2010),2010,680-683
[166] X. Zhu, X. Zhong, G. Xin, et. al. VOLES: vasularity-oriented level set algorithm forpulmonary vessel segmentaion in image guided intervention therapy[J]. IEEE ISBI2009
[167] S. R. Aylward, E. Bullitt. Initialization, noise, singularities, and scale in height ridge traversalfor tubular object centerline extraction[J]. IEEE T Med Imaging.2002,21(2):61-75
[168] S. You, E. Bas, D. Erdogmus. Extraction of samples from airway and vessel trees[C].33rdAnnual International Conference of the IEEE EMBS(2011), Boston, Massachusetts, USA,2011,5157-5160.
[169] P. K. Saha, Z. Gao, S. K. Alford. Topomorphologic separation of fused isointensity objects viamultiscale opening: separating arteries and veins in3-D pulmonary CT[J]. IEEE T MedImaging,2010,29(3):840-851
[170] P. Korfiatis, A. Karahaliou, L. Costaridou. Automated vessel tree segmentation: challenges incomputer aided quantification of diffuse parenchyma lung diseases[J]. IEEE ITAB (2009),Larnaca, Cyprus,2009
[171] Y. Pu, W. Wang, J. Zhou. Fractional differential approach to detecting textural features ofdigital image and its fractional differential filter implementation[J]. SCI CHINA SER F,2008,51(9):1319-1339
[172] Y. Pu, J. Zhou, X. Yuan. Fractional differential mask: a fractional differential-based approachfor multiscale texture enhancement[J]. IEEE T Image Process,2010,19(2):491-511.
[173] M. Sonka, V. Hlavac, R. Boyle. Image processing: analysis and machine vision (secondedition)[M]. BeiJing: Post&Telecom press,2003,87-88
[174] N. Otsu. A threshold selection method from gray-level histograms[J]. IEEE Transactions onSystems, Man and Cybernetics,1979,9(1):62-66
[175] Y. Zhang. Image engineering(ē)[M]. BeiJing: TSingHua press,2008,184-185
[176] S. NIE, L. LI, Z. CHEN. Pulmonary nodule segmentation algorithm based on three-domainmean shift clustering [J]. Journa1of East China Norma1University (Natura1Science),2008,1(1):60-67
[177] Y. Shi, F. Qi, Z. Xue, et al. Segmenting lung fields in serial chest radiographs using bothpopulation-based and patient-specific shape statistics [J]. IEEE transactions on MedicalImaging,2008,27(4):481-494
[178] P. Campadelli, E. Casiraghi, D. Artioli. A fully automated method for lung nodule detectionfrom postero-anterior chest radiographs [J]. IEEE transactions on Medical Imaging,2006,25(12):1588-1603
[179] I. Sluimer, M. Prokop, B. V. Ginneken. Toward automated segmentation of the pathologicallung in CT [J]. IEEE transactions on Medical Imaging,2005,24(8):1024-1038
[180] B. S. Zhao, G. Gamsu, M. S. Ginsburg, et al. Automatic detection of small lung nodules on CTutilizing a local density maximum algorithm[J]. J Appl Clin Med Phys,2003,4:248-260
[181] H. A. Bastawrous, T. Fukumoto, N. Nitta, et al. Detection of ground glass opacities in lung CTimages using gabor filters and neural networks[C]. Instrumentation and MeasurementTechnology Conference,2005
[182] Z. Y. Ge, B. Sahineer, H. P. Chan, et al. Computer-aided detection of lung nodules: falsepositive reduction using a3D gradient field method and3D ellipsoid fitting[J]. Med Phy,2005,32:2443-2454
[183] C. Bahlmann, X. L. Li, K. Okada. Local pulmonary structure classification for computer-aidednodule detection[C]. Proceeding of SPIE, Medical Imaging,2006,6144,1775-1785
[184] M. G. Penedo, M. J. Carreira, A. Mosquera, et al. Computer-aided diagnosis: a neural networkbased approach to lung nodule detection[J]. IEEE Trans on Medical Imaging,1998, l7(6):872-880
[185] K. T. Bae, J. S. Kim, Y. H. Na, et al. Pulmonary nodules：automated detection on CT imageswith morphologic matching algorithm-preliminary results[J]. Radiology,2005,236:286-294
[186] M. S. Brown, M. F. McNitt-Gary, J. G. Goldin, et al. Patient specific modules for lung noduledetection and surveillance in CT images[J]. IEEE Trans on Medical Imaging,2001,20:1242-1250
[187] G. Kuntimad, H. S. Ranganath. Perfect image segmentation using pulse coupled neuralnetworks [J]. IEEE transactions on Neural Network,1999,10(3):591-598
[188]彭真明,蒋彪,肖峻等.基于并行点火PCNN模型的图像分割新方法[J].自动化学报,2008,34(9):1169-1173
[189] Q. Li，F. Li，K. Doi．Computerized detection of lung nodules in thin-section CT images by useof selective enhancement filters and an automated rule-based classifier[J]. Acad Radiol,2008,15:165-175
[190]张娟.基于Hessian矩阵的肺结节计算机辅助检测算法研究[D].山东泰州：泰山医学院，2009，15-25
[191]李彬，欧陕兴，田联房等.肺结节计算机辅助检测与定位系统[J].计算机应用研究，2010,27(6):2184-2188
[192] J. Hanley, B. McNeil. The meaning and use of the area under a receiver operatingcharacteristic (ROC) curve[J]. Radiology,1982,143(1):29-36
[193] C. Metz. Basic principles of ROC analysis[J]. Seminars in Nuclear Medicine,1978,8(4):283-298
[194] W. Kostis, A. Reeves, D. Yankelevitz, et al. Three-dimensional segmentation and growth-rateestimation of small pulmonary nodules in helical CT images[J]. IEEE Transactions onMedical Imaging,2003,22(10):1259-1274
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |