Open Access
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
Article Number 02056
Number of page(s) 5
Section 3D Images Reconstruction and Virtual System
Published online 19 November 2018
  1. Sun, C. Bauer, R. Beichel. Automated 3-D segmentation of lungs with lung cancer in ct data using a novel robust active shape model approach. IEEE Trans. Med. Imaging. 31 (2): 449-460(2012) [CrossRef] [Google Scholar]
  2. A. Mansoor, U. Bagci, Z. Xu, B. Foster, K.N. Olivier, J.M. Elinoff, A.F. Suffredini, J.K. Udupa, D.J. Mollura. A generic approach to pathological lung segmentation. IEEE Trans. Med. Imaging. 33 (12):2293-2310(2014) [CrossRef] [Google Scholar]
  3. I. Sluimer, A. Schilham, M. Prokop, B. van Ginneken. Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans. Med. Imaging. 25 (4):385-405(2006) [CrossRef] [Google Scholar]
  4. S. Shen, A.A. Bui, J. Cong, W. Hsu. An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy. Comput. Biol. Med. 57:139-149(2015) [CrossRef] [Google Scholar]
  5. Wei Y, Shen G, Li J J. A fully automatic method for lung parenchyma segmentation and repairing. Journal of Digital Imaging. 26(3): 483-495(2013) [CrossRef] [Google Scholar]
  6. S. Zhou, Y. Cheng, S. Tamura. Automated lung segmentation and smoothing techniques for the inclusion of juxtapleural nodules and pulmonary vessels on chest ct images. Biomed. Signal Process. Control. 13:62-70(2014) [CrossRef] [Google Scholar]
  7. A.A. Farag, H.E.A.E. Munim, J.H. Graham, A.A. Farag. A novel approach for lung nodules segmentation in chest CT using level sets. IEEE Trans. Image Process. 22:5202-5213(2013) [CrossRef] [Google Scholar]
  8. M. Keshani, Z. Azimifar, F. Tajeripour, et al. Lung nodule segmentation and recognition using SVM classifier and active contour modeling: A complete intelligent system. Computers in biology and medicine. 43(4): 287-300(2013) [CrossRef] [Google Scholar]
  9. D. Kim, J. Kim, S. Noh, et al. Pulmonary nodule detection using chest CT images. Acta Radiologica. 44(3): 252-257(2003) [CrossRef] [Google Scholar]
  10. Yim Y, Hong H. Correction of segmented lung boundary for the inclusion of pleural nodules and pulmonary vessels in chest CT images. Computers in biology and medicine. 38(8): 845-857(2008) [CrossRef] [Google Scholar]
  11. Pu J, Roos J, Chin A Y, et al. Adaptive border marching algorithm: automatic lung segmentation on chest CT images. Computerized Medical Imaging and Graphics. 32(6): 452-462(2008) [CrossRef] [Google Scholar]
  12. Messay T, Hardie R C, Tuinstra T R. Segmentation of pulmonary nodules in computed tomography using a regression neural network approach and its application to the lung image database consortium and image database resource initiative dataset. Medical Image Analysis. 22(1): 48-62(2015) [CrossRef] [Google Scholar]
  13. Shuangfeng Dai, Ke Lü, Rui Zhai, Jiyang Dong. Lung Segmentation Method Based on 3D Region Growing Method and Improved Convex Hull Algorithm., Journal of Electronics and Information Technology. 38(9): 2358-2364(2016) [Google Scholar]
  14. Ganesh Singadkar, Abhishek Mahajan, Meenakshi Thakur, Sanjay Talbar. Automatic lung segmentation for the inclusion of juxtapleural nodules and pulmonary vessels using curvature-based border correction. Journal of King Saud University Computer and Information Sciences. 2018(2018) [Google Scholar]
  15. Yuan Kehong, Xiang Lanxi. Automated lung segmentation for chest CT images used for computer-aided diagnostics. Journal of Tsinghua University. 51(1): 90-95(2011) [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.