期刊文献+

基于CT影像的肺组织分割及其功能定量分析 被引量:5

Pulmonary Tissue Segmentation and Quantitative Function Analysis Based on CT Image
下载PDF
导出
摘要 高精度的肺组织分割是肺功能定量分析的前提和基础,有利于慢性阻塞性肺病的辅助诊断.传统的肺组织分割方法没有去除肺轮廓内部的细支气管,且无法处理严重粘连的肺气肿病例.本文提出一种全自动的三维肺组织分割方法:首先采用带有错误检测机制的二维图像阈值选取和三维区域增长进行粗分割,得到肺气道和肺实质组成的肺部充气区域;然后结合阈值和气管形态分析分割肺气道树,在防止泄露的同时提取更多肺内细支气管;最后通过扫描线粘连定位和动态规划,实现前、后联合粘连的定位及左右肺分离.实验中分别采用20组EXACT09数据和20组VESSEL12数据对所提出的方法进行评价,结果显示提取的气道树平均分支数为131个,与医生标记的金标准比较分支检出率为54.09%;肺实质分割结果与数据集提供肺轮廓M ark比较,Jaccard系数为95.35%,平均绝对边界距离为0.89mm.结果表明,本文方法能够有效的提取肺组织,运行时间与传统方法相比在临床实际应用中具有一定优势. High precision segmentation of pulmonary tissue is the premise and foundation for quantitative analysis of pulmonary function,which is useful to aided diagnosis of chronic obstructive pulmonary disease( COPD). Traditional methods do not remove the bronchioles inside the lung contours,and fails in cases with severe adhesion of emphysema. A fully automatic 3D pulmonary tissue segmentation method is presented in this paper. First,a rough segmentation is performed using a threshold selection with error detection mechanism in 2D image and 3D region growing and pulmonary airspace is gained which consist of pulmonary airways and pulmonary parenchyma. Then airways tree is segmented based on thresholds and morphological analysis,with more bronchioles extracted within the lung and leakage prevented. Finally,through adhesion positioning based on scanning line and dynamic programming,the anterior and posterior adhesions are located,and thus the left and right lungs are separated. 20 sets of EXACT09 data and 20 sets of VESSEL12 data were utilized to evaluate the proposed method. The average branch count of airway tree was 131,compared with the gold standard marked by doctors,the branch detection rate was 54. 09%; pulmonary parenchyma segmentation results were compared with lung contour mask that dataset provides,the average Jaccard coefficient was 95. 35%,and the average absolute boundary distance was 0. 89 mm. Results showthat the proposed method is able to extract pulmonary tissue effectively,and running time has some advantages in clinical practice compared with traditional methods.
出处 《小型微型计算机系统》 CSCD 北大核心 2016年第3期581-587,共7页 Journal of Chinese Computer Systems
基金 国家自然科学基金项目(61172002 61302012)资助 国家科技支撑计划项目(2014BAI17B01)资助 国家"八六三"高技术研究发展计划项目(2012AA02A607)资助 中央高校基本科研业务费(N130418002 N140402003 N140407001)资助
关键词 肺组织分割 肺实质分割 肺气道分割 肺功能定量分析 pulmonary tissue segmentation pulmonary parenchyma segmentation pulmonary airways segmentation quantitative analysis of the pulmonary function
  • 相关文献

参考文献3

二级参考文献50

  • 1杨金柱,赵大哲,徐心和.基于距离场的非线性图像插值分割方法[J].东北大学学报(自然科学版),2006,27(8):851-854. 被引量:3
  • 2章毓晋.图像分割[M].北京:科学出版社,2001..
  • 3冈萨雷斯 R C,伍兹 R E.数字图像处理[M].阮秋琦,译.2版.北京:电子工业出版社,2003.
  • 4Rubin G D, Lyo J K, Paik D S, et al. Pulmonary nodules on multi detector row CT scans: performance comparison of radiologists and computer-aided detection [ J ]. Radiology, 2005,234(10) :274 - 283.
  • 5Bram V G, Bart M H, Max A. Computer-aided diagnosis in chest radiography [ J ]. IEEE Transactions on Medical Imaging, 2001,20 (12) : 1228 - 1241.
  • 6Lin D T, Yan C R, Chen W T. Autonomous detection of pulmonary nodules on CT image with a neural network-based fuzzy system [ J ]. Computerized Medical Imaging and Graphics, 2005,29(6) :447 - 458.
  • 7Armato S G Ⅲ, Giger M L, Moran C J, et al. Computerized detection of pulmonary nodules on CT scan [J]. Radio Graphics, 2000,19(5) : 1303 - 1311.
  • 8Hedlund L W, Anderson R F, GouldingP L. Two methods for isolating the lung area of a CT scan for density information [J]. Radiology, 1982,144(2) :353 - 357.
  • 9McNitt-Grag M F, Sayre J W, Huang H K, et al. Pattern classification approach to segmentation of digital chest radiographs and chest CT image slices [ J ]. Proc SPIE, 1994,2167:465 476.
  • 10Armato S G Ⅲ, Giger M L. Automated detection of pulmonary nodules in helical computed tomography images of the thorax[J]. Proc SPIE, 1998,3338(2) :916-919.

共引文献18

同被引文献138

  • 1Stewart B W,Wild C P.World cancer report 2014[M].Lyons:International Agency for Research on Cancer Nonsertal Pub-lication,2014.
  • 2Lee S T,Kouzani A Z,Hu E,et al.Automated detection of lung nodules in computed tomography images:A review[J].Journal of Machine Vision and Applications,2012,23(1):151-163.
  • 3Valente I R S,Cortez P C,Neto E C,et al.Automatic 3Dpulmonary nodule detection in CT images:A survey[J].Com-puter Methods and Programs in Biomedicine,2016,124(1):91-107.
  • 4Shen S,Bui A A T,Cong J,et al.An automated lung segmentation approach using bidirectional chain codes to improve nod-ule detection accuracy[J].Computers in Biology and Medicine,2015,57(1):139-149.
  • 5Brader P,Abramson S J,Price A P,et al.Do characteristics of pulmonary nodules on computed tomography in children withknown osteosarcoma help distinguish whether the nodules are malignant or benign?[J].Journal of Pediatric Surgery,2011,46(4):729-735.
  • 6Arai K,Herdiyeni Y,Okumura H.Comparison of 2Dand 3Dlocal binary pattern in lung cancer diagnosis[J].InternationalJournal of Advanced Computer Science and Applications,2012,3(4):89-95.
  • 7Cohen J G,Reymond E,Lederlin M,et al.Differentiating pre-and minimally invasive from invasive adenocarcinoma usingCT-features in persistent pulmonary part-solid nodules in Caucasian patients[J].European Journal of Radiology,2015,84(4):738-744.
  • 8Kaya A,Can A B.A weighted rule based method for predicting malignancy of pulmonary nodules by nodule characteristics[J].Journal of Biomedical Informatics,2015,56(1):69-79.
  • 9Han F F,Wang H F,Zhang G P.Texture feature analysis for computer-aided diagnosis on pulmonary nodules[J].Journalof Digital Imaging,2015,28(1):99-115.
  • 10Hu S H,Hoffinan E A,Reinhardt J M.Automatic lung segmentation for accurate quantitation of volumetric x-ray CT ima-ges[J].IEEE Transactions on Medical Imaging,2001,20(6):490-498.

引证文献5

二级引证文献48

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部