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基于OTSU算法和带通滤波器的毛玻璃型肺结节检测 被引量：5

Detection of Ground Glass Opacity Nodule based on OTSU Algorithm and Band-Pass Filter
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摘要 针对毛玻璃型肺结节成淡淡的模糊影,不容易用肉眼观察到,并且其灰度值介于肺实质和血管之间的特点,提出一种基于带通滤波器的毛玻璃型肺结节检测方法.首先用OTSU算法分割肺实质,然后用带通滤波器检测毛玻璃型肺结节.实验表明,此检测方法在所需运行时间和敏感性上都优于现有方法. Ground glass opacity nodule is fuzzy and could not be observed by eyes,and its grey value is between the lung parenchyma and vessel.Aiming at these factors,a method of detection is put forward based on band-pass filter.Firstly,lung parenchyma is segmented by using OTSU algorithm;then,ground glass opacity nodule is detected based on band-pass filter.Experiments show that the proposed method is better than existing methods in required running time and sensitivity.
作者 范立南 胡向丽 孙申申
机构地区 沈阳大学信息工程学院
出处 《沈阳大学学报（自然科学版）》 CAS 2012年第6期43-46,共4页 Journal of Shenyang University：Natural Science
基金 辽宁省自然科学基金资助项目(20102154) 辽宁省教育厅科研计划项目(L2010376)
关键词 毛玻璃型肺结节 肺实质 血管 带通滤波器 OTSU算法 ground glass opacity nodule lung parenchyma blood vessels band-pass filter OTSU algorithm
分类号 TP183 [自动化与计算机技术—控制理论与控制工程]
  • 相关文献

参考文献8

  • 1Lin D T, Yan C R,Chen W T. Autonomous Detection ofPulmonary Nudules on CT Images with a Neural Network-based Fuzzy System [J]. Computerized Medical Imagingand Graphics. 2005 . 29 : 447 - 458.
  • 2薛以锋,鲍旭东,马汉林,吴磊.基于CT图像的肺结节计算机辅助诊断系统[J].中国医学物理学杂志,2006,23(2):93-96. 被引量：15
  • 3孙申申,范立南,任会之.基于圆点滤波器的毛玻璃型肺结节检测[J].计算机工程,2010,36(23):7-8. 被引量：2
  • 4Katsumata Y.Itai Y,MaedaS. etal. Automatic Detectionof GGO Candidate Regions Employing Four StatisticalFeatures on Thoracic MDCT Image [C]. COEX, Seoul*Korea : International Conference on Control. 2007,17-20.
  • 5Katsumata Y, Itai Y, Kim H. et al. Automatic Detectionof GGO Candidate Regions by Using Artificial NeuralNetworks from Thoracic MDCT Images [ C]. COEX.Seoul, Korea: The 3rd International Conference onInnovative Computing Information and Control, 2008,1983- 1985.
  • 6Suzuki K,SHI Zheng Hao,et al. Supervised Enhancem-ent of Lung Nodules by Use of a Massive-TrainingArtificial Neural Network (MTANN) in Computer-AidedDiagnosis (CAD) [C], Chicago: IEEE TRANSACTIONSON BIOMEDICAL, 2008,123 - 134.
  • 7陈卉,徐岩,马斌荣.针对肺结节检测的肺实质CT图像分割[J].中国医学物理学杂志,2008,25(6):883-886. 被引量：4
  • 8杨金柱,赵大哲,徐心和.一种多尺度圆形滤波器在肺结节增强中的应用[J].系统仿真学报,2008,20(14):3726-3729. 被引量：4

二级参考文献27

  • 1Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins著.阮秋琦等译.数字图像处理(MATLAB版)[M].北京:电子IT业出版社,2006.252-364.
  • 2Samuel G. Armato III, Geoffrey McLennan, Michael 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.
  • 3National Cancer Institute National Cancer Imaging Archive: https:// imaging.nci.nih.gov/ncia.
  • 4Artdinet A. Enquobahrie, Anthony P. Reeves, David F. Yankelevitz, et al. Automated detection of small pulmonary nodules in Whole Lung CT Scans[J]. Academic Radiology, 2007, 14(5): 579-593.
  • 5Katsumata Y, Itai Y, Maeda S. Automatic Detection of GGO Candidate Regions Employing Four Statistical Features on Thoracic MDCT lmage[C]//Proc. of International Conference on Control, Automation and System. [S.l. ]: IEEE Press, 2007.
  • 6Bastawrous H A, Fukumoto T, Nilta N, et al. Detection of Ground Glass Opacities in Lung CT Images Using Gabor Filters and Neural Networks [C]//Proc. of Instrumentation and Measurement Technology Conference. [S. l. ]: IEEE Press, 2005.
  • 7Guillon S, Baylou P, Najim M, et al. Adaptive Nonlinear Filters for 2D and 3D Image Enhancement[J]. Signal Processing, 1998, 67(3):237 -254.
  • 8Li Qiang, Sone S, Doi K. Selective Enhancement Fillers for Nodules, Vessels, and Airway Walls in Two and Three-dimensional CT Scans [J]. Medical Physics, 2003, 30(8): 2040 -2051.
  • 9Daw-Tung Lin,Chung-Ren Yan,Wen-Tai Chen.Autonomous detection of pulmonary nodules on CT images with a neural network-based fuzzy system[J].Computerized Medical Imaging and Graphics,2005,29:447-458.
  • 10Kawata Y,Niki H N,Ohmatsu,et al.Computerized Analysis of 3-D Pulmonary Nodule Images In Surrounding and Internal Structure Feature Spaces[J].IEEE transactions on medical imaging,2001:889-892.

共引文献20

同被引文献48

  • 1刘健庄,栗文青.灰度图象的二维Otsu自动阈值分割法[J].自动化学报,1993,19(1):101-105. 被引量：355
  • 2陈贵敏,贾建援,韩琪.粒子群优化算法的惯性权值递减策略研究[J].西安交通大学学报,2006,40(1):53-56. 被引量：307
  • 3范九伦,赵凤.灰度图像的二维Otsu曲线阈值分割法[J].电子学报,2007,35(4):751-755. 被引量：150
  • 4SEZGIN M, SANKUR B. Survey over image threshold techniques and quantitative performance evaluation[J]. Journal of Electronic lmaging0 2004,13( 1 ) : 146 - 168.
  • 5OTSU N. A threshold selection method from gray-level histograms[J]. IEEE Transactions on Systems, Man, and Cybernetic, 1979,9(1):62 - 66.
  • 6COELHO L, MARIANI V C. Improved firefly algorithm approach applied to chiller loading for energy conservation [J]. Energy and Buildings, 2013,59(2):273- 278.
  • 7HORNG M H, LIOU R J. Multilevel minimum cross entropy threshold selection based on the firefly algorithm [J]. Expert Systems with Applications, 2011,38 (12) : 14805 - 14811.
  • 8YANG X S. Firefly algorithm for multimodal optimization [C ] // Stochastic Algorithms: Foundations and Applications, SAGA 2009, Lecture Notes in Computer Sciences, 2009,5792:169 - 178.
  • 9Hodnett PA, Ko JP. Evaluation and management of indetermi hate pulmonary nodules. Radiol Clin North Am, 2012, 50 (5) 895-914.
  • 10Nakata M, Saeki H, Takata I, et al. Focal ground-glass opacity de teeted by low-dose helical CT. Chest, 2002,121(5):1464-1467.

引证文献5

二级引证文献24

沈阳大学学报（自然科学版）
2012年 第6期

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