Open Access
MATEC Web Conf.
Volume 232, 2018
2018 2nd International Conference on Electronic Information Technology and Computer Engineering (EITCE 2018)
Article Number 03011
Number of page(s) 6
Section Algorithm Study and Mathematical Application
Published online 19 November 2018
  1. WANG Qingchen, ZHENG Rongzhou. BAADE P D, et al, Cancer statistics in China,2015[J].CA:A Cancer Journal for Clinicians, 2016, 66(2): 115-132. [CrossRef] [Google Scholar]
  2. Takahiro Nakajima,Kazuhiro Yasufuku.Early Lung Cancer:Methods for Detection[J].Clinics in Chest Medicine,2013, 34(3): 373-383. [CrossRef] [Google Scholar]
  3. SHARMAN P. WOOD-BAKER R. International lung disease due to fumes from heat-cutting polymer rope[J].Occup Med,2013, 63(6): 451-453. [CrossRef] [Google Scholar]
  4. Bezdek J C.Pattern recognition with fuzzy objective function algorithms[M].New York:Plenum Press,1981. [Google Scholar]
  5. Ahmed M N,Yamany S M,Mohamed N,et al.A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data[J].IEEE Transaction on Medical Imaging,2002, 21(3): 193-199. [CrossRef] [Google Scholar]
  6. Chen S C,Zhang D Q.Robust image segmentation using FCM with spatial constraints based on new kernal-induced distance measure[J].IEEE Transactions Systerms,Man,andCybernetics-Part B:Cybernetics,2004, 34(4): 1907-1916. [CrossRef] [Google Scholar]
  7. Szilagyi L,Benyo Z,Szilagyii S M,et al.MR brain image segmentation using an enhanced fuzzy C-means algorithms[C]//Proceeding of 25th Annual Informational Conference of IEEE EMBS.Cancun:IEEE Press,2003,1:724-726. [Google Scholar]
  8. Cai W,Chen S,Zhang D Q.Fast and robust fuzzy C-means clustering algorithm incorporating local information for image segmentation[J].Pattern Recognition,2007, 40(3): 825-838. [CrossRef] [Google Scholar]
  9. Krinidis S,Chatzis V. A robust fuzzy local information C-means clustering algorithm[J].IEEE Transaction on Image Processing,2010, 19(5): 1328-1337. [CrossRef] [Google Scholar]
  10. CHAMA C K,MUKHOPADHYAY S,BISW AS P K,et al.Automated lung field segmentation in CT image using mean shift clustering and geomerical features[J].Medical Imaging,Computer-Aided Diagnosis,2013, 53(11): 1-10. [Google Scholar]
  11. KORFIATIS. P,KAZANTZI. A,KALOGEROPOUL OUC,et al.Optimizing lung volume segmentation by texture classification[C]//Proceedings of the IEEE/EMBS Region&International conference on Information Technology Application in Biomedicine.[S.I.]:IEEE,2010:1-4. [Google Scholar]
  12. Cao L,Zhan J,Yu X E,et al.Fast lung segmentation algorithm for thoracic CT based on automated thresholding [J].Computer Engineering and Applications,2008,44 ( 12) : 178-181. [Google Scholar]
  13. Wang B,Gu X M,Yang Y,et al.Automated lung segmentation for chest CT images based on random walk algorithm [J].Journal of Computer Applications,2015,35 ( 9) : 2666-2672. [Google Scholar]
  14. Pulagam A R, Kande G B, Ede V K R, et al.Automated lung segmentation from HRCT scans with diffuse parenchymal lung diseases [J] .Journal of Digital Imaging,2016,29 ( 4) : 507-519. [CrossRef] [Google Scholar]
  15. Lei T,Wang Y,Fan Y Y,et al.Vector morphological operators in HSV color space[J].Science China Information Science,2013, 56(1): 1-12. [CrossRef] [Google Scholar]
  16. Zarinbal M,Fazel Zarandi M H,Turksen I B.Interval type-2 relative entropy fuzzy C-means clustering [J].Information Sciences, 2014, 272(10): 49-72. [CrossRef] [Google Scholar]
  17. Lei T,Wang Y,Wang G H,et al.Multivariate mathematical morphology based on fuzzy extremumestimation[J]. IET Image Processing,2014, 8(9): 548-558. [CrossRef] [Google Scholar]
  18. Lei T,Fan Y Y.Noise grandient reduction based on morphological dual operators[J].IET Image Processing, 2011, 5(1): 1-17. [CrossRef] [Google Scholar]
  19. Hwang C,Rhee F C H.Uncertain fuzzy clustering:interval type-2 fuzzy approach to Cmeans.IEEE Transactions on Fuzzy System,2007, 15(1): 107-120. [CrossRef] [Google Scholar]
  20. News Center.The current situation of lung cancer incidence in 2014,and the mortality trend of lung cancer in the future [EB /OL] .2014-04-09 [2016-05-10]. [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.