Skip to main content

Advertisement

SpringerLink
Log in

Cervical cell recognition based on AGVF-Snake algorithm

  • Original Article
  • Published:
International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

In recent years, with the increasing incidence of cervical cancer, it is a tedious and time-consuming task with unsatisfying accuracy to manually recognize the cells. Machine recognition can be a good solution, but it suffers from the difficulty of obtaining precise edges of cells, which directly influence the final recognition accuracy. To improve the recognition accuracy and shorten the time used for cell recognition, an AGVF-Snake (Adaptive Gradient Vector Flow-Snake) model for the extraction of cell edges has been proposed in this paper.

Methods

Firstly, the cell is initially located by the improved Canny algorithm. Then, the adaptive initial contour model and gradient vector model are used to obtain accurate cell edges. Finally, the PSO–SVM (Particle Swarm Optimization-Support Vector Machine) classifier is selected to recognize the cervical cells.

Results

Herlev dataset is used to verify the AGVF-Snake algorithm; the accuracy of two and seven classifications are recorded. Six other classification methods are introduced for comparison. According to the experimental results, the accuracy of two and seven classifications can achieve up to 99%, which are better than other six methods.

Conclusion

The experiment results show that the proposed algorithm has obvious recognition advantages, and thus provides an effective methodological framework for the diagnosis of cervical cancer diseases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Lin ZH (2011) The cell image segmentation based on the K-L transform and OTSU method. In: 2011 IEEE multimedia and signal processing (CMSP). IEEE, pp 25–28. https://doi.org/10.1109/CMSP.2011.14

  2. Mustafa N, Matisan A, Mashor MY (2009) Automated multicells segmentation of thin prep image using modified seed based region growing algorithm. Biomed Fuzzy Hum Sci 14:41–47

    Google Scholar 

  3. Guan T, Zhou DX, Liu YH (2015) Accurate segmentation of partially overlapping cervical cells based on dynamic sparse contour searching and GVF Snake model. IEEE J Biomed Health Inf 19:1494–1504

    Article  Google Scholar 

  4. Thanatip C, Nipon T, Sansanee A (2009) Cervical cell classification using Fourier transform. In: 13th international conference on biomedical engineering. Springer, Berlin, pp 476–480

  5. Chen YF, Huang PC, Lin KC, Lin HH, Wang LE, Cheng CC, Chen TP, Chan YK, Chiang JY (2014) Semi-automatic segmentation and classification of pap smear cells. IEEE J Biomed Health Inf 18:94–108

    Article  CAS  Google Scholar 

  6. Singh S, Tejaswini V, Murthy RP, Mutgi A (2015) Neural network based automated system for diagnosis of cervical cancer. Int J Biomed Clin Eng (IJBCE) 4:26–39

    Article  CAS  Google Scholar 

  7. Chen CL, Mahjoubfar A, Tai LC (2016) Deep learning in label-free cell classification. Sci Rep. https://doi.org/10.1038/srep21471

    Article  PubMed  PubMed Central  Google Scholar 

  8. Wu J, Lu MY, Liu C (2010) SVM-based scheme within hybrid learning framework for image retrieval. Acta Electron Sin 38:2101–2106

    Google Scholar 

  9. Chen GC, Yu JS (2005) Particle swarm optimization algorithm. Inf Control 34:318–323

    Google Scholar 

  10. Geusebroek JM, Smeulders AWM,Vande WJ(2003) Fast anisotropic Gauss filtering. In: 2003 IEEE transactions image processing. IEEE, 12(8), pp 938–943. https://doi.org/10.1109/TIP.2003.812429

    Article  Google Scholar 

  11. Wang L, Xu TT (2016) An improved Canny algorithm in the GVF Snake model. Comput Eng 42:231–235

    Google Scholar 

  12. Jin YH (2011) Research on image edge detection based on improved canny algorithm. Dissertation, Chongqing Normal University

  13. Xu CY, Peince JL (1997) Gradient vector flow: a new external force for Snakes. In: 1997 IEEE proceedings of 1997 conference on computer vision and pattern recognition. IEEE, pp 66–71. https://doi.org/10.1109/CVPR.1997.609299

  14. Xu CY, Prince JL (1998) Generalized gradient vector flow external forces for active contours. Signal Process 71:131–139. https://doi.org/10.1016/S0165-1684(98)00140-6

    Article  Google Scholar 

  15. Cheng JR, Foo SW, Krishnan SM (2006) Dynamic directional gradient vector flow for Snakes. In: 2006 IEEE transactionson image processing. IEEE, pp 1563–1571. https://doi.org/10.1109/ISSPIT.2004.1433748

  16. Xiang Y, Chung ACS, Ye J (2006) An active contour model for image segmentation based on elastic interaction. J Comput Phys 219:455–476. https://doi.org/10.1016/j.jcp.2006.03.026

    Article  CAS  Google Scholar 

  17. Xu CY, Yezzi AJ, Prince JL (2000) In: 2000 IEEE on the relationship between parametric and geometric active contours, pp 483–489. https://doi.org/10.1109/ACSSC.2000.911003

  18. Zijdenbos AP, Dawant BM, Margolin RA, Palmer AC (1994) Morphometric analysis of white matter lesion in MR images: method and validation. IEEE Trans Med Imaging 13(4):716–724. https://doi.org/10.1109/42.363096

    Article  CAS  PubMed  Google Scholar 

  19. Thanatip C, Nipon T, Sansanee A (2014) Automatic cervical cell segmentation and classification in pap smears. Comput Methods Programs Biomed 113:539–556. https://doi.org/10.1016/j.cmpb.2016.10.001

    Article  Google Scholar 

  20. Li K, Lu Z, Liu WY, Yin JP (2012) Cytoplasm and nucleus segmentation in cervical smear images using radiating GVF Snake. Pattern Recognit 45(4):1255–1264. https://doi.org/10.1016/j.patcog.2011.09.018

    Article  Google Scholar 

  21. Kale K, Aksoy S(2010)Segmentation of cervical cell images. In: Proceedings of the 20th IAPR international conference on pattern recognition, Istanbul, Turkey. https://doi.org/10.1109/icpr.2010.587

  22. Erik M (2003) Pap-smear classification, Denmark

  23. Zhao M, Wu AG, Song JJ, Sun XG, Dong N (2016) Automatic screening of cervical cells using block image processing. Biomed Eng. https://doi.org/10.1186/s12938-016-0131-z

    Article  Google Scholar 

  24. Bora K, Manish C, Li P, Malay KK, Anup KD (2017) Automated classification of Pap smear images to detect cervical dysplasia. Comput Methods Programs Biomed 138:31–47. https://doi.org/10.1016/j.cmpb.2016.10.001

    Article  PubMed  Google Scholar 

  25. Zhang L, Lu L, Nogues J, Ronald MS, Liu SX, Yao JH (2017) DeepPap: deep convolutional networks for cervical cell classification. IEEE J Biomed Health Inf 21:1633–1643. https://doi.org/10.1109/JBHI.2017.2705583

    Article  Google Scholar 

  26. Sajeena TA, Jereesh AS (2015) Automated cervical cancer detection through RGVF segmentation and SVM classification. In: 2015 International conference on computing and network communications, pp 16–19. https://doi.org/10.1109/CoCoNet.2015.7411260

  27. Jayasingh ME, Allwin S (2015) Nominated texture based cervical cancer classification. Comput Math Methods Med 2015:1–10

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the associate editor and reviewers for their valuable comments and suggestions that improved the paper’s quality.

Funding

This study was funded by the Nature Science Foundation (Grant Number 61773282).

Author information

Authors and Affiliations

  1. School of Electrical and Information Engineering, Tianjin University, 92 Weijin Road, Tianjin, China

    Na Dong, Li Zhao & Aiguo Wu

Authors
  1. Na Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Li Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aiguo Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Na Dong.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Informed consent

This article does not contain patient data.

Human and animal rights

This article does not contain any studies with human participants or animals performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Dong, N., Zhao, L. & Wu, A. Cervical cell recognition based on AGVF-Snake algorithm. Int J CARS 14, 2031–2041 (2019). https://doi.org/10.1007/s11548-019-01961-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11548-019-01961-x

Keywords

Search

Navigation