A method for smoothing segmented lung boundary in chest CT images

被引:0
|
作者
Yim, Yeny
Hong, Helen [1 ,1 ]
机构
[1] Seoul Womens Univ, Coll Informat & Media, Div Multimedia Engn, Seoul 139774, South Korea
关键词
image processing; segmentation; smoothing; contour tracking; lung;
D O I
10.1117/12.710273
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
To segment low density lung regions in chest CT images, most of methods use the difference in gray-level value of pixels. However, radiodense pulmonary vessels and pleural nodules that contact with the surrounding anatomy are often excluded from the segmentation result. To smooth lung boundary segmented by gray-level processing in chest CT images, we propose a new method using scan line search. Our method consists of three main steps. First, lung boundary is extracted by our automatic segmentation method. Second, segmented lung contour is smoothed in each axial CT slice. We propose a scan line search to track the points on lung contour and find rapidly changing curvature efficiently. Finally, to provide consistent appearance between lung contours in adjacent axial slices, 2D closing in coronal plane is applied within pre-defined subvolume. Our method has been applied for performance evaluation with the aspects of visual inspection, accuracy and processing time. The results of our method show that the smoothness of lung contour was considerably increased by compensating for pulmonary vessels and pleural nodules.
引用
收藏
页数:7
相关论文
共 50 条
  • [31] Regional variance of visually lossless threshold in compressed chest CT images: Lung versus mediastinum and chest wall
    Kim, Tae Jung
    Lee, Kyoung Ho
    Kim, Bohyoung
    Kim, Kil Joong
    Chun, Eun Ju
    Bajpai, Vasundhara
    Kim, Young Hoon
    Hahn, Seokyung
    Lee, Kyung Won
    EUROPEAN JOURNAL OF RADIOLOGY, 2009, 69 (03) : 483 - 488
  • [32] Computerized analysis of chest CT images
    Toriwaki, J
    Shimizu, A
    Mori, K
    COMPUTER-AIDED DIAGNOSIS IN MEDICAL IMAGING, 1999, 1182 : 35 - 44
  • [33] Development and Validation of Segmentation Method for Lung Cancer Volumetry on Chest CT
    Kim, Young Jae
    Lee, Seung Hyun
    Lim, kun Young
    Kim, Kwang Gi
    JOURNAL OF DIGITAL IMAGING, 2018, 31 (04) : 505 - 512
  • [34] Development and Validation of Segmentation Method for Lung Cancer Volumetry on Chest CT
    Young Jae Kim
    Seung Hyun Lee
    Kun Young Lim
    Kwang Gi Kim
    Journal of Digital Imaging, 2018, 31 : 505 - 512
  • [35] Automatic Localization of Lung Opacity in Chest CT Images - A Real-World Study
    Xie, Yiting
    Rajan, Deepta
    Schudlo, Larissa
    Takeuchi, Yusuke
    Graf, Benedikt
    Coy, Adam
    Negahdar, Mohammadreza
    Mukherjee, Vandana
    Beymer, David
    Krishnan, Arun
    MEDICAL IMAGING 2021: COMPUTER-AIDED DIAGNOSIS, 2021, 11597
  • [36] CURVATURE-BASED CORRECTION ALGORITHM FOR AUTOMATIC LUNG SEGMENTATION ON CHEST CT IMAGES
    Hu, Shicheng
    Bi, Kesen
    Ge, Quanxu
    Li, Mingchao
    Xie, Xin
    Xiang, Xin
    JOURNAL OF BIOLOGICAL SYSTEMS, 2014, 22 (01) : 1 - 28
  • [37] Lung nodule pre-diagnosis and insertion path planning for chest CT images
    Rong-Li Xie
    Yao Wang
    Yan-Na Zhao
    Jun Zhang
    Guang-Biao Chen
    Jian Fei
    Zhuang Fu
    BMC Medical Imaging, 23
  • [38] Lung nodule pre-diagnosis and insertion path planning for chest CT images
    Xie, Rong-Li
    Wang, Yao
    Zhao, Yan-Na
    Zhang, Jun
    Chen, Guang-Biao
    Fei, Jian
    Fu, Zhuang
    BMC MEDICAL IMAGING, 2023, 23 (01)
  • [39] Feature Extraction and LDA based Classification of Lung Nodules in Chest CT scan Images
    Aggarwal, Taruna
    Furqan, Asna
    Kalra, Kunal
    2015 INTERNATIONAL CONFERENCE ON ADVANCES IN COMPUTING, COMMUNICATIONS AND INFORMATICS (ICACCI), 2015, : 1189 - 1193
  • [40] Comparison of Convolutional Neural Network for Classifying Lung Diseases from Chest CT Images
    Mohan, Ramya
    Rama, A.
    Ganapathy, Kirupa
    INTERNATIONAL JOURNAL OF PATTERN RECOGNITION AND ARTIFICIAL INTELLIGENCE, 2022, 36 (16)