A Computer Vision Approach for Lung Cancer Classification Using FNAC-Based Cytological Images

  • Moumita Dholey
  • Atasi Sarkar
  • Maitreya Maity
  • Amita Giri
  • Anup Sadhu
  • Koel Chaudhury
  • Soumen Das
  • Jyotirmoy Chatterjee
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 704)


Lung cancer represents malignant tumour having uncontrolled lung cell growth/proliferation. It can be diagnosed by invasive and non-invasive diagnostic approaches. One of the most effective and accurate approach is Papanicolaou (Pap)-stained cell cytology from fine needle aspiration cytology (FNAC). The manual assessment of cytopathology slides under light microscopy is time-consuming and suffers from feature ambiguities including inter-observer variability. To overcome such problems, the automated cytological analysis is the need of time. This study presents an automated computer vision approach to identify and classify cancerous cell present in microscopic images of Pap smear. The proposed methodology follows colour normalization, image filtering, nucleus segmentation and classification of segmented cells. The nucleus is segmented using the Random Walker with K-means clustering method. The post-processing is carried out on the segmented images to delineate joined nucleus and to remove unwanted regions. Subsequently, multiple nuclear features, i.e. colour, texture and geometric attributes are extracted from each segmented nucleus. After that, a comparative study on supervised classifier selection for the extracted features was adopted towards improving classification accuracy for distinguishing nucleus of non-small cell and small cell lung cancer. Artificial neural network performs best with sensitivity of \( 97.58\% \), specificity of \( 97.6\% \), accuracy of \( 97.46\% \).



The first author acknowledges MHRD funded GWC project for financial support.


  1. 1.
    Abdel-Zaher, A.M., Eldeib, A.M.: Breast cancer classification using deep belief networks. Expert Systems with Applications 46, 139–144 (2016)Google Scholar
  2. 2.
    Astion, M.L., Wilding, P.: The application of backpropagation neural networks to problems in pathology and laboratory medicine. Archives of pathology & laboratory medicine 116(10), 995–1001 (1992)Google Scholar
  3. 3.
    Bai, X., Sun, C., Zhou, F.: Splitting touching cells based on concave points and ellipse fitting. Pattern recognition 42(11), 2434–2446 (2009)Google Scholar
  4. 4.
    Bora, K., Chowdhury, M., Mahanta, L.B., Kundu, M.K., Das, A.K.: Automated classification of pap smear images to detect cervical dysplasia. Computer Methods and Programs in Biomedicine 138, 31–47 (2017)Google Scholar
  5. 5.
    Byrt, T., Bishop, J., Carlin, J.B.: Bias, prevalence and kappa. Journal of clinical epidemiology 46(5), 423–429 (1993)Google Scholar
  6. 6.
    Galloway, M.M.: Texture analysis using gray level run lengths. Computer graphics and image processing 4(2), 172–179 (1975)Google Scholar
  7. 7.
    George, Y.M., Bagoury, B.M., Zayed, H.H., Roushdy, M.I.: Automated cell nuclei segmentation for breast fine needle aspiration cytology. Signal Processing 93(10), 2804–2816 (2013)Google Scholar
  8. 8.
    Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing Using MATLAB. McGraw Hill Education (2010)Google Scholar
  9. 9.
    Grady, L.: Random walks for image segmentation. IEEE transactions on pattern analysis and machine intelligence 28(11), 1768–1783 (2006)Google Scholar
  10. 10.
    Hall, M.A.: Correlation-based feature selection for machine learning. Ph.D. thesis, The University of Waikato (1999)Google Scholar
  11. 11.
    Han, J., Pei, J., Kamber, M.: Data mining: concepts and techniques. Elsevier (2011)Google Scholar
  12. 12.
    Haralick, R.M., Shanmugam, K.: Textural features for image classification. IEEE Transactions on systems, man, and cybernetics 3(6), 610–621 (1973)Google Scholar
  13. 13.
    Kecheril, S.S., Venkataraman, D., Suganthi, J., Sujathan, K.: Automated lung cancer detection by the analysis of glandular cells in sputum cytology images using scale space features. Signal, Image and Video Processing 9(4), 851–863 (2015)Google Scholar
  14. 14.
    Kuruvilla, J., Gunavathi, K.: Lung cancer classification using neural networks for ct images. Computer methods and programs in biomedicine 113(1), 202–209 (2014)Google Scholar
  15. 15.
    Mariarputham, E.J., Stephen, A.: Nominated texture based cervical cancer classification. Computational and mathematical methods in medicine 2015 (2015)Google Scholar
  16. 16.
    Niwas, S.I., Palanisamy, P., Sujathan, K., Bengtsson, E.: Analysis of nuclei textures of fine needle aspirated cytology images for breast cancer diagnosis using complex daubechies wavelets. Signal Processing 93(10), 2828–2837 (2013)Google Scholar
  17. 17.
    Paul, P.R., Bhowmik, M.K., Bhattacharjee, D.: Automated cervical cancer detection using pap smear images. In: Proceedings of Fourth International Conference on Soft Computing for Problem Solving. pp. 267–278. Springer (2015)Google Scholar
  18. 18.
    Prasad, D.K., Quek, C., Leung, M.K.H.: Fast segmentation of sub-cellular organelles. International Journal of Image Processing (IJIP) 6(5), 317 (2012)Google Scholar
  19. 19.
    Siegel, R.L., Miller, K.D., Jemal, A.: Cancer statistics, 2015. CA: a cancer journal for clinicians 65(1), 5–29 (2015)Google Scholar
  20. 20.
    Song, Y., Tan, E.L., Jiang, X., Cheng, J.Z., Ni, D., Chen, S., Lei, B., Wang, T.: Accurate cervical cell segmentation from overlapping clumps in pap smear images. IEEE Transactions on Medical Imaging 36(1), 288–300 (2017)Google Scholar
  21. 21.
    Tareef, A., Song, Y., Cai, W., Huang, H., Chang, H., Wang, Y., Fulham, M., Feng, D., Chen, M.: Automatic segmentation of overlapping cervical smear cells based on local distinctive features and guided shape deformation. Neurocomputing 221, 94–107 (2017)Google Scholar
  22. 22.
    Wang, P., Hu, X., Li, Y., Liu, Q., Zhu, X.: Automatic cell nuclei segmentation and classification of breast cancer histopathology images. Signal Processing 122, 1–13 (2016)Google Scholar
  23. 23.
    Yaroslavsky, L.: Digital picture processing: an introduction, vol. 9. Springer Science & Business Media (2012)Google Scholar
  24. 24.
    Yu, K.H., Zhang, C., Berry, G.J., Altman, R.B., R, C., Rubin, D.L., Snyder, M.: Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nature Communications 7 (2016)Google Scholar
  25. 25.
    Zhang, W., Li, H.: Automated segmentation of overlapped nuclei using concave point detection and segment grouping. Pattern Recognition (2017)Google Scholar
  26. 26.
    Zhou, Z.H., Jiang, Y., Yang, Y.B., Chen, S.F.: Lung cancer cell identification based on artificial neural network ensembles. Artificial Intelligence in Medicine 24(1), 25–36 (2002)Google Scholar
  27. 27.
    Zuiderveld, K.: Contrast limited adaptive histogram equalization. In: Graphics gems IV. pp. 474–485. Academic Press Professional, Inc. (1994)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Moumita Dholey
    • 1
  • Atasi Sarkar
    • 1
  • Maitreya Maity
    • 1
  • Amita Giri
    • 2
  • Anup Sadhu
    • 3
  • Koel Chaudhury
    • 1
  • Soumen Das
    • 1
  • Jyotirmoy Chatterjee
    • 1
  1. 1.School of Medical Science and TechnologyIndian Institute of Technology KharagpurKharagpurIndia
  2. 2.Calcutta National Medical College and HospitalKolkataIndia
  3. 3.EKO Diagnostic Centre, Medical CollegeKolkataIndia

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