Learning Shape-Driven Segmentation Based on Neural Network and Sparse Reconstruction Toward Automated Cell Analysis of Cervical Smears

  • Afaf TareefEmail author
  • Yang Song
  • Weidong Cai
  • Heng Huang
  • Yue Wang
  • Dagan Feng
  • Mei Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9489)


The development of an automatic and accurate segmentation approach for both nuclei and cytoplasm remains an open problem due to the complexities of cell structures resulting from inconsistent staining, poor contrast, and the presence of mucus, blood, inflammatory cells, and highly overlapping cells. This paper introduces a computer vision slide analysis technique of two stages: the 3-class cellular component classification, and individual cytoplasm segmentation. Feed forward neural network along with discriminative shape and texture features is applied to classify the cervical cell images in the cellular components. Then, a learned shape prior incorporated with variational framework is applied for accurate localization and delineation of overlapping cells. The shape prior is dynamically modelled during the segmentation process as a weighted linear combination of shape templates from an over-complete shape repository. The proposed approach is evaluated and compared to the state-of-the-art methods on a dataset of synthetically generated overlapping cervical cell images, with competitive results in both nuclear and cytoplasmic segmentation accuracy.


Cervical cell segmentation Overlapping cells Neural network Sparse reconstruction Level set evolution 


  1. 1.
    Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., Susstrunk, S.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRefGoogle Scholar
  2. 2.
    Béliz-Osorio, N., Crespo, J., García-Rojo, M., Muñoz, A., Azpiazu, J.: Cytology imaging segmentation using the locally constrained watershed transform. In: Soille, P., Pesaresi, M., Ouzounis, G.K. (eds.) ISMM 2011. LNCS, vol. 6671, pp. 429–438. Springer, Heidelberg (2011) CrossRefGoogle Scholar
  3. 3.
    Bergmeir, C., Silvente, G.M., Benítez, J.M.: Segmentation of cervical cell nuclei in high-resolution microscopic images: a new algorithm and a web-based software framework. Comput. Methods Programs Biomed. 107(3), 497–512 (2012)CrossRefGoogle Scholar
  4. 4.
    Chankong, T., Theera-Umpon, N., Auephanwiriyakul, S.: Automatic cervical cell segmentation and classification in pap smears. Comput. Methods Programs Biomed. 113(2), 539–556 (2014)CrossRefGoogle Scholar
  5. 5.
    Fan, J., Wang, R., Li, S., Zhang, C.: Automated cervical cell image segmentation using level set based active contour model. In: 12th International Conference on Control Automation Robotics & Vision (ICARCV), 2012, pp. 877–882. IEEE (2012)Google Scholar
  6. 6.
    Fu, T., Yin, X., Zhang, Y.: Voronoi algorithm model and the realization of its program. Comput. Simulation 23, 89–91 (2006)Google Scholar
  7. 7.
    Genctav, A., Aksoy, S., Onder, S.: Unsupervised segmentation and classification of cervical cell images. Pattern Recogn. 45(12), 4151–4168 (2012)CrossRefGoogle Scholar
  8. 8.
    Goodall, C.: Procrustes methods in the statistical analysis of shape. J. Roy. Stat. Soc. B (Methodological) 53, 285–339 (1991)MathSciNetzbMATHGoogle Scholar
  9. 9.
    Harandi, N.M., Sadri, S., Moghaddam, N.A., Amirfattahi, R.: An automated method for segmentation of epithelial cervical cells in images of ThinPrep. J. Med. Syst. 34(6), 1043–1058 (2010)CrossRefGoogle Scholar
  10. 10.
    Hu, M., Ping, X., Ding, Y.: Automated cell nucleus segmentation using improved snake. In: International Conference on Image Processing 2004, ICIP 2004. vol. 4, pp. 2737–2740. IEEE (2004)Google Scholar
  11. 11.
    Jung, C., Kim, C.: Segmenting clustered nuclei using H-minima transform-based marker extraction and contour parameterization. IEEE Trans. Biomed. Eng. 57(10), 2600–2604 (2010)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Jung, C., Kim, C., Chae, S.W., Oh, S.: Unsupervised segmentation of overlapped nuclei using bayesian classification. IEEE Trans. Biomed. Eng. 57(12), 2825–2832 (2010)CrossRefGoogle Scholar
  13. 13.
    Kale, A., Aksoy, S.: Segmentation of cervical cell images. In: 20th International Conference on Pattern Recognition (ICPR), 2010, pp. 2399–2402. IEEE (2010)Google Scholar
  14. 14.
    Li, C., Xu, C., Gui, C., Fox, M.D.: Distance regularized level set evolution and its application to image segmentation. IEEE Trans. Image Process. 19(12), 3243–3254 (2010)MathSciNetCrossRefGoogle Scholar
  15. 15.
    Li, K., Lu, Z., Liu, W., Yin, J.: Cytoplasm and nucleus segmentation in cervical smear images using radiating GVF snake. Pattern Recogn. 45(4), 1255–1264 (2012)CrossRefGoogle Scholar
  16. 16.
    Lu, Z., Carneiro, G., Bradley, A.P.: Automated nucleus and cytoplasm segmentation of overlapping cervical cells. In: Mori, K., Sakuma, I., Sato, Y., Barillot, C., Navab, N. (eds.) MICCAI 2013, Part I. LNCS, vol. 8149, pp. 452–460. Springer, Heidelberg (2013) CrossRefGoogle Scholar
  17. 17.
    Nosrati, M., Hamarneh, G.: A variational approach for overlapping cell segmentation. In: ISBI Overlapping Cervical Cytology Image Segmentation Challenge, pp. 1–2. IEEE (2014)Google Scholar
  18. 18.
    Nosrati, M., Hamarneh, G.: Segmentation of overlapping cervical cells: a variational method with star-shape prior. In: IEEE International Symposium on Biomedical Imaging (ISBI), IEEE (2015)Google Scholar
  19. 19.
    World Health Organization.: Who Guidance Note: Comprehensive Cervical Cancer Prevention and Control: A Healthier Future for Girls and Women, WHO Press, Geneva (2013)Google Scholar
  20. 20.
    Overlapping Cervical Cytology Image Segmentation Challenge ISBI 2014:
  21. 21.
    Plissiti, M.E., Nikou, C.: Overlapping cell nuclei segmentation using a spatially adaptive active physical model. IEEE Trans. Image Process. 21(11), 4568–4580 (2012)MathSciNetCrossRefGoogle Scholar
  22. 22.
    Plissiti, M.E., Nikou, C., Charchanti, A.: Combining shape, texture and intensity features for cell nuclei extraction in Pap smear images. Pattern Recogn. Lett. 32(6), 838–853 (2011)CrossRefGoogle Scholar
  23. 23.
    Rousson, M., Paragios, N.: Shape priors for level set representations. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002, Part II. LNCS, vol. 2351, pp. 78–92. Springer, Heidelberg (2002) CrossRefGoogle Scholar
  24. 24.
    Sokouti, B., Haghipour, S., Tabrizi, A.D.: A framework for diagnosing cervical cancer disease based on feedforward MLP neural network and ThinPrep histopathological cell image features. Neural Comput. Appl. 24(1), 221–232 (2014)CrossRefGoogle Scholar
  25. 25.
    Tareef, A., Song, Y., Cai, W., Feng, D., Chen, M.: Automated three-stage nucleus and cytoplasm segmentation of overlapping cells. In: 13th International Conference on Control Automation Robotics & Vision (ICARCV), 2014, pp. 865–870. IEEE (2014)Google Scholar
  26. 26.
    Ushizima, D., Bianch, A., Carneiro, C.: Segmentation of subcellular compartiments combining superpixel representation with voronoi diagrams. In: ISBI Overlapping Cervical Cytology Image Segmentation Challenge, pp. 1–2. IEEE (2014)Google Scholar
  27. 27.
    Wu, H.S., Gil, J., Barba, J.: Optimal segmentation of cell images. In: IEE Proceedings: Vision, Image and Signal Processing, vol. 145, pp. 50–56. IET (1998)Google Scholar
  28. 28.
    Zhang, Z., Rao, B.D.: Sparse signal recovery with temporally correlated source vectors using sparse bayesian learning. IEEE J. Sel. Top. Signal Process. 5(5), 912–926 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Afaf Tareef
    • 1
    Email author
  • Yang Song
    • 1
  • Weidong Cai
    • 1
  • Heng Huang
    • 2
  • Yue Wang
    • 3
  • Dagan Feng
    • 1
    • 4
  • Mei Chen
    • 5
    • 6
  1. 1.School of Information TechnologiesUniversity of SydneySydneyAustralia
  2. 2.Department of Computer Science and EngineeringUniversity of Texas at ArlingtonArlingtonUSA
  3. 3.Department of Electrical and Computer EngineeringVirginia Tech Research Center - ArlingtonArlingtonUSA
  4. 4.Med-X Research InstituteShanghai Jiaotong UniversityShanghaiChina
  5. 5.Department of InformaticsUniversity of Albany State University of New YorkAlbanyUSA
  6. 6.Robotics InstituteCarnegie Mellon UniversityPittsburghUSA

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