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基于高分辨率CT图像的肺结节检测 被引量:1

An Algorithm of Nodule Detection Based on High Resolution CT Images
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摘要 结合二维检测的快速性和三维检测的准确性,提出了一种基于高分辨率CT图像的肺结节自动检测算法,即先用二维收敛度滤波器产生候选肺结节,再用三维Hessian矩阵检测滤波器去除假阳性肺结节。用病理CT数据验证,检测灵敏度达到90%,每层图片的平均假阳性结节数为0.33。 Combining the speed of two-dimensional detection with the precision of three-dimensional detection, an automatic algorithm based on high resolution CT images is proposed to identify nodules in this paper. Nodule candidates are extracted by a two-dimensional convergence index (CI) filter, then a three-dimensional Hessian matrix detection filter is introduced to reduce false positive lung nodules, Experiments show that the algorithm is effective with a sensitivity of 90% and the false positive lung nodules per slice is 0.33.
出处 《中国医疗器械杂志》 CAS 2008年第3期175-178,共4页 Chinese Journal of Medical Instrumentation
基金 安徽省教育厅重点课题(2006KJ097A)的资助
关键词 CT图像 肺结节检测 收敛度 HESSIAN矩阵 CT images, detection of pulmonary nodule, convergence index, Hessian matrix
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参考文献7

  • 1American Cancer Society.Cancer Facts and Figures 2006.Atlanta,ACS,2006
  • 2Yongbum Lee et al.Automated Detection of Pulmonary Nodules in Helical CT Images Based on an Improved Template-Matching Technique.IEEE Trans Med Imaging,2001,20(7):595-604
  • 3Kanazawa K et al.Computer-Aided Diagnosis for Pulmonary Nodules Based on Helical CT Images.Computerized Medical Imaging and Graphics,1998,22(2):157-167
  • 4Brown MS et al:Patient-Specific Models for Lung Nodule Detection and Surveillance in CT Images.IEEE Trans Med Imaging,2001,20(12):1242-1250
  • 5Thomas Billow et al.A Method for Lung Nodule Visualization from Multi-slice CT Data.International Congress Series,2005,1281:1127-1131
  • 6Daw Tung Lin et al.Autonomous Detection of Pulmonary Nodules on CT Images with a Neural Network-Based Fuzzy System.Computerized Medical Imaging and Graphics,2005,29(6):447-458
  • 7Hidefumi Kobatake et al.Convergence Index Filter for Vector Fields.IEEE TRANSACTIONS ON IMAGE PROCESSING,1999,8(8):1029-1038

同被引文献14

  • 1王德杭,汪家旺,于立燕,俞同福.肺结节检测算法研究[J].上海生物医学工程,2005,26(1):3-7. 被引量:4
  • 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高园园,吕庆文,郭宏,冯前进,陈武凡.一种新的肺结节检测算法[J].计算机工程与应用,2007,43(23):198-199. 被引量:6
  • 4Sluimer I,Schilham A,Mathias P,et al.Computer analysis of computed tomography scans of the lung[J].A Survey,IEEE Transactions on Medical Imaging,2006,25(4):385-405.
  • 5Al-Kadi OS,Watson D.Texture analysis of aggressive and nonaggressive lung tumor CE CT images[J].IEEE Transactions on Biomedical Engineering,2008,55(7):1822-1830.
  • 6Yim Yeny,Hong Helen.Correction of segmented lung boundary for inclusion of pleural nodules and pulmonary vessels in chest CT images[J].Computers in Biology and Medicine,2007,38:845-857.
  • 7Dehmeshki J,Ye XJ,Lin XY,et al.Automated detection of lung nodules in CT images using shape-based genetic algorithm[J].Computerized Medical Imaging and Graphics,2007,31:408-417.
  • 8Mastumoto S,Kundel HL,Gee JC,Gefter WB,et al.Pulmonary nodule detection in CT images with quantized convergence index fiter[J].Medical Image analysis,2006,10:343-352.
  • 9Retico A,Delogu P,Fantacci ME,et al.Lung nodule detection in low-dose and thin-slice computed tomography[J].Computers in Biology and Medicine,2008,38:525-534.
  • 10Brown Matthew.Computer-aided diagnosis in thoracic CT[R].Department of Radiology David Geffen School of Medicine at UCLA.

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