Lung Nodule Identification and Classification from Distorted CT Images for Diagnosis and Detection of Lung Cancer

  • G. SavithaEmail author
  • P. Jidesh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)


An automated computer-aided detection (CAD) system is being proposed for identification of lung nodules present in computed tomography (CT) images. This system is capable of identifying the region of interest (ROI) and extracting the features from the ROI. Feature vectors are generated from the gray-level covariance matrix using the statistical properties of the matrix. The relevant features are identified by adopting principle component analysis algorithm on the feature space (the space formed from the feature vectors). Support vector machine and fuzzy C-means algorithms are used for classifying nodules. Annotated images are used to validate the results. Efficiency and reliability of the system are evaluated visually and numerically using relevant measures. Developed CAD system is found to identify nodules with high accuracy.


  1. 1.
    American Cancer Society, Cancer Facts and Figures, America Cancer Society (2016).
  2. 2.
    Aubert, G., Aujol, J.F.: A variational approach to removing multiplicative noise. SIAM J. Appl. Math 68(5), 925–946 (2008)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Chambolle, A.: An algorithm for total variation minimization and applications. J. Math. Imaging Vis. 20(1), 89–97 (2004)MathSciNetzbMATHGoogle Scholar
  4. 4.
    Chan, T.F., Vese, L.A.: Active contours without edges. IEEE Trans. Image Process. 10(2), 266–277 (2001)CrossRefGoogle Scholar
  5. 5.
    Chen, L., et al.: Speech emotion recognition: features and classification models. Digit. Signal Process. 22(6), 1154–1160 (2012)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Gilboa, G., Osher, S.: Nonlocal operators with applications to image processing. Multiscale Model. Simul. 7(3), 1005–1028 (2008)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Gravel, P., Beaudoin, G., De Guise, J.A.: A method for modeling noise in medical images. IEEE Trans. Med. Imaging 23(10), 1221–1232 (2004)Google Scholar
  8. 8.
    Jacobs, C., et al.: Computer-aided detection of ground glass nodules in thoracic CT images using shape, intensity and context features. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Berlin (2011)Google Scholar
  9. 9.
    Jacobs, C., et al.: Automatic detection of subsolid pulmonary nodules in thoracic computed tomography images. Med. Image Anal. 18.2, 374–384 (2014)Google Scholar
  10. 10.
    Joo, S., et al.: Computer-aided diagnosis of solid breast nodules: use of an artificial neural network based on multiple sonographic features. IEEE Trans. Med. Imaging 23(10), 1292–1300 (2004)Google Scholar
  11. 11.
    Kuhnigk, J.-M., et al.: Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans. IEEE Trans. Med. Imaging 25(4), 417–434 (2006)CrossRefGoogle Scholar
  12. 12.
    Magdy, E., Zayed, N., Fakhr, M.: Automatic classification of normal and cancer lung CT images using multiscale AM-FM features. J. Biomed. Imaging 2015, 11 (2015)Google Scholar
  13. 13.
    Messay, T., Hardie, R.C., Rogers, S.K.: A new computationally efficient CAD system for pulmonary nodule detection in CT imagery. Med. Image Anal. 14(3), 390–406 (2010)CrossRefGoogle Scholar
  14. 14.
    Murphy, K., et al.: A large-scale evaluation of automatic pulmonary nodule detection in chest CT using local image features and k-nearest-neighbour classification. Med. Image Anal. 13(5), 757–770 (2009)Google Scholar
  15. 15.
    Soh, L.-K., Tsatsoulis, C.: Texture analysis of SAR sea ice imagery using gray level co-occurrence matrices. IEEE Trans. Geosci. Remote Sens. 37(2), 780–795 (1999)CrossRefGoogle Scholar
  16. 16.
    Tao, Y., et al.: Multi-level ground glass nodule detection and segmentation in CT lung images. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, Berlin (2009)Google Scholar
  17. 17.
    Zhou, J., et al.: An automatic method for ground glass opacity nodule detection and segmentation from CT studies. In: 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2006. EMBS’06. IEEE, 2006Google Scholar

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© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.National Institute of Technology KarnatakaSurathkalIndia

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