Abstract
One of the main challenges for cell segmentation is to separate overlapping cells, which is also a challenging task for cytologists. Here we propose a method that combines different algorithms for cervical cell segmentation of Pap smear images and searches for the best result underlying the maximization of a similarity coefficient. We carried out experiments with three state-of-the-art segmentation algorithms on images with clumps of cervical cells. We extracted features such as coefficient of variation and overlapping ratios for each cell grouping and selected the most appropriate algorithm to segment each cell clump. For decision criterion, we identified the cell clumps of the training dataset and calculated the mentioned features. We segmented each clump by the algorithms and reckoned the Dice measure from each segmentation. Finally, we used the kNN classifier to predict the best algorithm among neighboring k-clumps by choosing the one with the largest number of wins. We validated our proposal on multifocal cervical cell images and obtained an average Dice around 76.6% without using a threshold value. These results demonstrated that the proposed ensemble of segmentation algorithms is promising and suitable for cervical cell image segmentation.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Bradley, A.P., Bamford, P.C.: A one-pass extended depth of field algorithm based on the over-complete discrete wavelet transform. In: Image and Vision Computing New Zealand (IVCNZ), pp. 279–284 (2004)
Bray, F., Ferlay, J., Soerjomataram, I., Siegel, R.L., Torre, L.A., Jemal, A.: Global cancer statistics 2018: globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68(6), 394–424 (2018)
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)
Cong, G., Parvin, B.: Model-based segmentation of nuclei. Pattern Recogn. 33(8), 1383–1393 (2000)
Gay, J., Donaldson, L., Goellner, J.: False-negative results in cervical cytologic studies. Acta Cytol. 29(6), 1043–1046 (1985)
Gençtav, A., Aksoy, S., Önder, S.: Unsupervised segmentation and classification of cervical cell images. Pattern Recogn. 45(12), 4151–4168 (2012)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, vol. 3. Prentice Hall, Upper Saddle River (2008)
Irshad, H., Veillard, A., Roux, L., Racoceanu, D.: Methods for nuclei detection, segmentation, and classification in digital histopathology: a review-current status and future potential. IEEE Rev. Biomed. Eng. 7, 97–114 (2014)
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)
Kale, A., Aksoy, S.: Segmentation of cervical cell images. In: Proceedings of 20th International Conference on Pattern Recognition, pp. 2399–2402. IEEE Computer Society, Istanbul (2010)
Kumar, S., Ong, S.H., Ranganath, S., Ong, T.C., Chew, F.T.: A rule-based approach for robust clump splitting. Pattern Recogn. 39(6), 1088–1098 (2006)
Lee, H., Kim, J.: Segmentation of overlapping cervical cells in microscopic images with superpixel partitioning and cell-wise contour refinement. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, pp. 63–69 (2016)
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)
Lu, Z., Carneiro, G., Bradley, A.P.: An improved joint optimization of multiple level set functions for the segmentation of overlapping cervical cells. IEEE Trans. Image Process. 24(4), 1261–1272 (2015)
Nosrati, M.S., Hamarneh, G.: Segmentation of overlapping cervical cells: a variational method with star-shape prior. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 186–189 (2015)
Papanicolaou, G.N.: A new procedure for staining vaginal smears. Science 95(2469), 438–439 (1942)
Phoulady, H.A., Goldgof, D.B., Hall, L.O., Mouton, P.R.: An approach for overlapping cell segmentation in multi-layer cervical cell volumes. In: IEEE International Symposium on Biomedical Imaging (ISBI). IEEE (2015)
Phoulady, H.A., Goldgof, D.B., Hall, L.O., Mouton, P.R.: A new approach to detect and segment overlapping cells in multi-layer cervical cell volume images. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 201–204. IEEE (2016)
Plissiti, M.E., Charchanti, A., Krikoni, O., Fotiadis, D.I.: Automated segmentation of cell nuclei in Pap smear images. In: Proceedings IEEE International Special Topic Conference on Information Technology in Biomedicine, Greece, pp. 26–28 (2006)
Plissiti, M.E., Nikou, C., Charchanti, A.: Automated detection of cell nuclei in Pap smear images using morphological reconstruction and clustering. IEEE Trans. Inf Technol. Biomed. 15(2), 233–241 (2011)
Radau, P., Lu, Y., Connelly, K., Paul, G., Dick, A., Wright, G.: Evaluation framework for algorithms segmenting short axis cardiac MRI. MIDAS J. Cardiac MR Left Ventricle Segmentation Challenge 49 (2009)
Ramalho, G.L., Ferreira, D.S., Bianchi, A.G., Carneiro, C.M., Medeiros, F.N., Ushizima, D.M.: Cell reconstruction under voronoi and enclosing ellipses from 3D microscopy. In: IEEE International Symposium on Biomedical Imaging (ISBI). IEEE (2015)
Tareef, A., et al.: Automatic segmentation of overlapping cervical smear cells based on local distinctive features and guided shape deformation. Neurocomputing 221, 94–107 (2017)
Ushizima, D.M., Bianchi, A.G., Carneiro, C.M.: Segmentation of subcellular compartments combining superpixel representation with voronoi diagrams. In: IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1–2. IEEE (2014)
Wan, T., Xu, S., Sang, C., Jin, Y., Qin, Z.: Accurate segmentation of overlapping cells in cervical cytology with deep convolutional neural networks. Neurocomputing 365, 157–170 (2019)
Wang, P., Wang, L., Li, Y., Song, Q., Lv, S., Hu, X.: Automatic cell nuclei segmentation and classification of cervical Pap smear images. Biomed. Signal Process. Control 48, 93–103 (2019)
Zhang, C., Sun, C., Su, R., Pham, T.D.: Clustered nuclei splitting via curvature information and gray-scale distance transform. J. Microsc. 259(1), 36–52 (2015)
Zhang, L., Kong, H., Chin, C.T., Liu, S., Chen, Z., Wang, T., Chen, S.: Segmentation of cytoplasm and nuclei of abnormal cells in cervical cytology using global and local graph cuts. Comput. Med. Imaging Graph. 38(5), 369–380 (2014)
Zhao, L., Li, K., Yin, J., Liu, Q., Wang, S.: Complete three-phase detection framework for identifying abnormal cervical cells. IET Image Proc. 11(4), 258–265 (2017)
Zijdenbos, A.P., Dawant, B.M., Margolin, R.A., Palmer, A.C.: Morphometric analysis of white matter lesions in MR images: method and validation. IEEE Trans. Med. Imaging 13(4), 716–724 (1994)
Acknowledgment
This work was supported by CAPES/CNPq-PVE (401442/2014-4) and CNPq.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Martins, G.L., Ferreira, D.S., Medeiros, F.N.S., Ramalho, G.L.B. (2020). Ensemble of Algorithms for Multifocal Cervical Cell Image Segmentation. In: Cerri, R., Prati, R.C. (eds) Intelligent Systems. BRACIS 2020. Lecture Notes in Computer Science(), vol 12319. Springer, Cham. https://doi.org/10.1007/978-3-030-61377-8_19
Download citation
DOI: https://doi.org/10.1007/978-3-030-61377-8_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-61376-1
Online ISBN: 978-3-030-61377-8
eBook Packages: Computer ScienceComputer Science (R0)