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Identification of Malignancy from Cytological Images Based on Superpixel and Convolutional Neural Networks

  • Shyamali Mitra
  • Soumyajyoti Dey
  • Nibaran DasEmail author
  • Sukanta Chakrabarty
  • Mita Nasipuri
  • Mrinal Kanti Naskar
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 784)

Abstract

This chapter explores two methodologies for classification of cytology images into benign and malignant. Heading toward the automated analysis of the images to eradicate human intervention, this chapter draws curtain from the history of automated CAD-based design system for better understanding of the roots of the evolving image processing techniques in the analysis of biomedical images. Our first approach introduces the clustering-based approach to segment the nucleus region from the rest. After segmentation, nuclei features are extracted based on which classification is done using some standard classifiers. The second perspective suggests the usage of deep-learning-based techniques such as ResNet and InceptionNet-v3. In this case, classification is done with and without segmented images but not using any handcrafted features. The analysis provides results in favor of CNN where the average performances are found better than the existing result using feature-based approach.

Keywords

Cytology FNAC Superpixel-based segmentation ResNet50 InceptionNet-V3 Random crop Random horizontal flip 

References

  1. 1.
    Sagawa, M., Usuda, K., Aikawa, H., et al.: Screening for lung cancer: present and future. Gan To Kagaku Ryoho 39, 19–22 (2012)Google Scholar
  2. 2.
    Xian, G.M.: An identification method of malignant and benign liver tumors from ultrasonography based on GLCM texture features and fuzzy SVM. Expert Syst. Appl. 37, 6737–6741 (2010).  https://doi.org/10.1016/j.eswa.2010.02.067CrossRefGoogle Scholar
  3. 3.
    Muhammad Hussain NK (2012) AUTOMATIC MASS DETECTION IN MAMMOGRAMS USING MULTISCALE SPATIAL WEBER LOCAL DESCRIPTOR. IWSSIP 2012Google Scholar
  4. 4.
    Domanski, H.A.: Fine-needle aspiration cytology of soft tissue lesions: diagnostic challenges. Diagn. Cytopathol. 35, 768–773 (2007).  https://doi.org/10.1002/dc.20765CrossRefGoogle Scholar
  5. 5.
    Lopes Cardozo, P.: The significance of fine needle aspiration cytology for the diagnosis and treatment of malignant lymphomas. Folia Haematol. Int. Mag. Klin Morphol. Blutforsch. 107, 601–620 (1980)Google Scholar
  6. 6.
    Tucker, J.H.: Cerviscan: an image analysis system for experiments in automatic cervical smear prescreening. Comput. Biomed Res. (1976).  https://doi.org/10.1016/0010-4809(76)90033-1CrossRefGoogle Scholar
  7. 7.
    Zahniser, D.J., Oud, P.S., Raaijmakers, M.C.T., et al.: BioPEPR: a system for the automatic prescreening of cervical smears. J. Histochem. Cytochem. 27, 635–641 (1979).  https://doi.org/10.1177/27.1.86581CrossRefGoogle Scholar
  8. 8.
    Vrolijk, J., Pearson, P.L., Ploem, J.S.: LEYTAS: a system for the processing of microscopic images. Anal. Quant. Cytol. (1980)Google Scholar
  9. 9.
    Street, W.N.: Xcyt: a system for remote cytological diagnosis and prognosis of breast cancer. In: Artificial Intelligence Techniques in Breast Cancer Diagnosis and Prognosis, pp. 297–326. World Scientific Publishing (2000)Google Scholar
  10. 10.
    Zhang, L., Chen, S., Wang, T., et al.: A practical segmentation method for automated screening of cervical cytology. In: 2011 International Conference on Intelligent Computation and Bio-Medical Instrumentation (2011).  https://doi.org/10.1109/icbmi.2011.4
  11. 11.
    Zhang, L., Kong, H., Chin, C.T., et al.: Segmentation of cytoplasm and nuclei of abnormal cells in cervical cytology using global and local graph cuts. Comput. Med. Imaging Graph. 38, 369–380 (2014).  https://doi.org/10.1016/j.compmedimag.2014.02.001CrossRefGoogle Scholar
  12. 12.
    Zhao, L., Li, K., Wang, M., et al.: Automatic cytoplasm and nuclei segmentation for color cervical smear image using an efficient gap-search MRF. Comput. Biol. Med. (2016).  https://doi.org/10.1016/j.compbiomed.2016.01.025CrossRefGoogle Scholar
  13. 13.
    Li, K., Lu, Z., Liu, W., Yin, J.: Cytoplasm and nucleus segmentation in cervical smear images using Radiating GVF Snake. Pattern Recognit. (2012).  https://doi.org/10.1016/j.patcog.2011.09.018CrossRefGoogle Scholar
  14. 14.
    Chankong, T., Theera-Umpon, N., Auephanwiriyakul, S.: Automatic cervical cell segmentation and classification in PAP smears. Comput. Methods Programs Biomed. 113 (2014)CrossRefGoogle Scholar
  15. 15.
    George, Y.M., Zayed, H.H., Roushdy, M.I., Elbagoury, B.M.: Remote computer-aided breast cancer detection and diagnosis system based on cytological images. IEEE Syst. J. 8, 949–964 (2014).  https://doi.org/10.1109/JSYST.2013.2279415CrossRefGoogle Scholar
  16. 16.
    Yang, X., Li, H., Zhou, X.: Nuclei segmentation using marker-controlled watershed, tracking using mean-shift, and Kalman filter in time-lapse microscopy. IEEE Trans. Circ. Syst. I Regul. Pap. 53, 2405–2414 (2006).  https://doi.org/10.1109/TCSI.2006.884469CrossRefGoogle Scholar
  17. 17.
    Hrebień, M., Korbicz, J., Obuchowicz, A.: Hough transform, (1 + 1) search strategy and watershed algorithm in segmentation of cytological images. In: Kurzynski, M., Puchala, E., Wozniak, M., Zolnierek, A. (eds.) Advances in Soft Computing, pp. 550–557. Springer, Berlin, Heidelberg (2007)Google Scholar
  18. 18.
    Garud, H., Karri, S.P.K., Sheet, D., et al.: High-magnification multi-views based classification of breast fine needle aspiration cytology cell samples using fusion of decisions from deep convolutional networks. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops (2017)Google Scholar
  19. 19.
    Dey, P., Logasundaram, R., Joshi, K.: Artificial neural network in diagnosis of lobular carcinoma of breast in fine-needle aspiration cytology. Diagn. Cytopathol. 41, 102–106 (2011).  https://doi.org/10.1002/dc.21773CrossRefGoogle Scholar
  20. 20.
    Isa, N.A.M., Subramaniam, E., Mashor, M.Y., Othman, N.H.: Fine needle aspiration cytology evaluation for classifying breast cancer using artificial neural network. Am. J. Appl. Sci. 4, 999–1008 (2007)CrossRefGoogle Scholar
  21. 21.
    Braz, E.F., Lotufo, R.D.A.: Nuclei detection using deep learning. In: Brazilian Symposium on Telecommunications and Processing of Signals, pp. 1059–1063 (2017)Google Scholar
  22. 22.
    Tareef, A., Song, Y., Huang, H., et al.: Optimizing the cervix cytological examination based on deep learning and dynamic shape modeling. Neurocomputing 248, 28–40 (2017).  https://doi.org/10.1016/j.neucom.2017.01.093CrossRefGoogle Scholar
  23. 23.
    Weickert, J.: Anisotropic diffusion in image processing. Image Rochester NY 256:170 (1998). http://doi.org/10.1.1.11.751
  24. 24.
    Achanta, R., Shaji, A., Smith, K., et al.: SLIC superpixels compared to state-of-the-art superpixel methods. 6, 1–8 (2011)Google Scholar
  25. 25.
    Sander, J., Ester, M., Kriegel, H.-P., Xu, X.: Density-Based clustering in spatial databases: the algorithm GDBSCAN and Its applications. Data Min. Knowl. Discov. 2, 169–194 (1998).  https://doi.org/10.1023/A:1009745219419CrossRefGoogle Scholar
  26. 26.
    Luukka, P.: Feature selection using fuzzy entropy measures with similarity classifier. Expert Syst. Appl. 38, 4600–4607 (2011).  https://doi.org/10.1016/j.eswa.2010.09.133CrossRefGoogle Scholar
  27. 27.
    Liu, M., Tuzel, O., Ramalingam, S., Chellappa, R.: Entropy rate superpixel segmentation. In: CVPR 2011, pp. 2097–2104 (2011)Google Scholar
  28. 28.
    Mitra, S., Dey, S., Das, N., et al.: Identification of Benign and Malignant Cells from cytological images using superpixel based segmentation approach. In: Mandal, J.K., Sinha, D. (eds.) 52nd Annual Convention of CSI 2018: Social Transformation—Digital Way, pp. 257–269. Springer, Singapore (2018)Google Scholar
  29. 29.
    Krizhevsky, A., Sutskever, I., Hinton, G.E.: Alexnet. Adv. Neural Inf. Process. Syst. (2012). http://dx.doi.org/10.1016/j.protcy.2014.09.007
  30. 30.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)Google Scholar
  31. 31.
    Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2015)Google Scholar
  32. 32.
    He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition (2015). CoRR abs/1512.0Google Scholar
  33. 33.
    Szegedy, C., Vanhoucke, V., Ioffe, S., et al.: Rethinking the inception architecture for computer vision (2015). CoRR abs/1512.0Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Shyamali Mitra
    • 1
  • Soumyajyoti Dey
    • 2
  • Nibaran Das
    • 2
    Email author
  • Sukanta Chakrabarty
    • 3
  • Mita Nasipuri
    • 2
  • Mrinal Kanti Naskar
    • 1
  1. 1.Department of Electronics and Telecommunication EngineeringJadavpur UniversityKolkataIndia
  2. 2.Department of Computer Science and EngineeringJadavpur UniversityKolkataIndia
  3. 3.Theism Medical Diagnostics CentreKolkataIndia

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