Maintenance

Thursday evening the 17th at 7 pm there will be a 5 minute disruption in TCIA as the primary firewalls are updated to the latest in-version software. On Thursday the 24th, there be another 5-10 minute disruption as the primary firewalls are moved to new hardware.

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  1. Wang W, Luo J, Yang X, Lin H. Data Analysis of the Lung Imaging Database Consortium and Image Database Resource Initiative. Academic Radiology. 2015.
  2. Shen S, Bui AA, Cong J, Hsu W. An automated lung segmentation approach using bidirectional chain codes to improve nodule detection accuracy. Computers in biology and medicine. 2015;57:139-49.
  3. Messay T, Hardie RC, Tuinstra TR. 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. 2015.(paper)
  4. Gavrielides MA, Zeng R, Myers KJ, Sahiner B, Petrick N. Benefit of overlapping reconstruction for improving the quantitative assessment of CT lung nodule volume. Academic Radiology, 2013. 20(2):173-180. doi: 10.1016/j.acra.2012.08.014. (link)
  5. Aggarwal P, Vig R, and Sardana H Patient-Wise Versus Nodule-Wise Classification of Annotated Pulmonary Nodules using Pathologically Confirmed Cases. Journal of Computers, 2013. 8(9):2245-2255. (link)
  6. Sivakumar S and Chandrasekar C, Lung nodule detection using fuzzy clustering and support vector machines. International Journal of Engineering and Technology, 2013. 5(1):179-185.(link)
  7. Armato S, et al., Collaborative projects. Int J CARS, 2012. 7(1):S111-S115.
  8. Sivakumar S and Chandrasekar C, Lungs image segmentation through weighted FCM.Recent Advances in Computing and Software Systems (RACSS), 2012 International Conference. 25-27 April 2012 pages 109-113. IEEE. doi:10.1109/RACSS.2012.6212707 (link)
  9. Armato III SG, McLennan G, Bidaut L, McNitt-Gray MF, Meyer CR, Reeves AP, Zhao B, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, van Beek EJR, Yankelevitz D, et al.:The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): A completed reference database of lung nodules on CT scans.Medical Physics, 38: 915–931, 2011. (link)
  10. Diciotti S, Lombardo S, Falchini M, Picozzi G, Mascalchi M. Automated segmentation refinement of small lung nodules in CT scans by local shape analysis. Biomedical Engineering, IEEE Transactions. 2011. 58(12):3418-3428. doi: 10.1109/TBME.2011.2167621. (link)
  11. Kumar, D., M. J. Shafiee, et al. (2015). "Discovery Radiomics for Computed Tomography Cancer Detection." arXiv preprint arXiv:1509.00117.

  12. Sivakumar, S. and C. Chandrasekar (2015). "A Novel Noise Removal Method for Lung CT SCAN Images Using Statistical Filtering Techniques." International Journal of Algorithms Design and Analysis 1(1).

  13. Magdy, E., N. Zayed, et al. "Automatic Classification of Normal and Cancer Lung CT Images using Multi-scale AM-FM."

  14. Demir, Ö. and A. Yılmaz Çamurcu (2015). "Computer-aided detection of lung nodules using outer surface features." Bio-Medical Materials and Engineering 26(s1): 1213-1222.

  15. Kumar, A., F. Nette, et al. (2014). "A Visual Analytics Approach using the Exploration of Multi-Dimensional Feature Spaces for Content-based Medical Image Retrieval."

  16. Armato SG III, McLennan G, McNitt-Gray MF, Meyer CR, Yankelevitz D, Aberle DR, Henschke CI, Hoffman EA, Kazerooni EA, MacMahon H, Reeves AP, Croft BY, Clarke LP, The Lung Image Database Consortium Research Group: Lung Image Database Consortium: Developing a resource for the medical imaging research community. Radiology 232: 739–748, 2004.

Collection:  NLST

Collection:  Phantom FDA

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