Open Access
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
Article Number 02001
Number of page(s) 5
Section 3D Images Reconstruction and Virtual System
Published online 19 November 2018
  1. Ost, D. E., Gould, M. K., 2012. Decision making in patients with pulmonary nodules., Am J Respir Crit Care Med, 185:363–372. [CrossRef] [Google Scholar]
  2. Ost, D, 2015. Fishman’s Pulmonary Diseases and Disorders, McGraw-Hill. Columbus (5th ed.). [Google Scholar]
  3. Otsu, N., 1978. A threshold selection method from gray level histogram. IEEE Transactions on System, Man, Cybernetics. Vol. 19, No. 1, pp.62–66. [Google Scholar]
  4. L.Vincent, P, S., 1991. Watersheds in digital space: An efficient algorithm based on immersion simulation. IEEE Trans. on Pattern Analysisand Machine Intelligence, 13(6): 583-598. [Google Scholar]
  5. Adams, R., Bischof, L., 1994. Seeded region growing. IEEE Trans. Pattern Anal. Machine Intell. 16 (6), 641-647. [CrossRef] [Google Scholar]
  6. Kass, M., Witkin, A., and Terzopoulos, D., 1987. Snakes: Active contour models. Int. Journal of Computer Vision. 1(4), 321-331. [Google Scholar]
  7. Malladi, R., Sethian, J. A., Vemuri, B. C., 1995. Shape modeling with front propagation: A level set approach. IEEE Transactions on Pattern Analysis and Machine Intelligence. 17(2):158–175. [CrossRef] [Google Scholar]
  8. Wei Y, Chang C, Jia T, et al, 2009. Segmentation of regions of interest in lung ct images based on 2D Otsu optimized by genetic algorithm, Proc. Chin. Control Decision Conf. pp. 5185-5189. [Google Scholar]
  9. Helen, R., Kamaraj, N., Selvi, K., Raja Raman, V. 2011. Segmentation of Pulmonary Parenchyma in CT lung Images based on 2D Otsu optimized by PSO. International Conference on Emerging Trends in Electrical and Computer Technology. 536–541. [CrossRef] [Google Scholar]
  10. Parveen, S. S., Kavitha, C., 2013. Detection of lung cancer nodules using automatic region growing method. International Conference on Computing. 4, 1-6. [Google Scholar]
  11. Wu, S., Wang J., 2012. Pulmonary nodules 3D detection on serial CT scans. 2012 Third Global Congress on Intelligent System [Google Scholar]
  12. Vincent, L., and Soille, P., 1991. Watersheds in digital spaces: an efficient algorithm based on immersion simulations. In IEEE Transactions on Pattern Analysis and Machine Intelligence. 13(6), 583–598. [CrossRef] [Google Scholar]
  13. Meyer, f., 1992. Color image segmentation. International Conference on Image Processing and ITS Applications IET. 2446. [Google Scholar]
  14. Shojaii, R., Alirezai, J., and Babyn P., 2005. Automatic Lung Segmentation in CT images Watershed Transform. in Proc. ICIP.2, 1270-1273. [Google Scholar]
  15. He, Y., Chen, P., and Chen, Y. 2008. Perfusion-Ventilation Lung SPECT Image Analysis System Based on Minimum Cross-Entropy Threshold and Watershed Segmentation. International Colloquium on Computing Communication Control and Management 1,280-284. [Google Scholar]
  16. Kanitkar, S.,S., Thombare, N., D., Lokhande, S., S., 2015. Detection of lung cancer using marker-controlled watershed transform. Pervasive Computing.1,6. [Google Scholar]
  17. Avinash, C., S.,Manjunath, K., Kumar, S., S.,2017. An improved image processing analysis for the detection of lung cancer using Gabor filters and watershed segmentation technique. International Conference on Inventive Computation Technologies. 1, 6. [Google Scholar]
  18. Sun, X., Wang, X., 2011. Study of edge detection algorithms for lung CT image on the basis of MATLAB. Control and Decision Conference. 810-813. [Google Scholar]
  19. Mirderikvand. N, Naderan. M, Jamshidnezhad. 2016. A Accurate automatic localisation of lung nodules using Graph Cut and snakes algorithms. International Conference on Computer and Knowledge Engineering. 194-199. [Google Scholar]
  20. Soltaninejad. S, Cheng. I, Basu 2016. A Robust Lung Segmentation Combining Adaptive Concave Hulls with Active Contours. IEEE International Conference on Systems, Man, and Cybernetics. 004775-004780 [Google Scholar]
  21. Liu, S., and Li, J., 2006. Automatic Medical Image Segmentation Using Gradient and Intensity Combined Level Set Method. Conf Proc IEEE Eng Med Biol Soc., 1(1):3118 – 3121 [CrossRef] [Google Scholar]
  22. Farag, A., A., El Munim, H., E., A., Graham, J., H., et al., 2013. A novel approach for lung nodules segmentation in chest CT using level sets. IEEE Trans. Image Process. 22(12), 5202-5213. [CrossRef] [Google Scholar]

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