Skip to main content

Advertisement

SpringerLink
Log in

Automatic Identification of Human Erythrocytes in Microscopic Fecal Specimens

  • Systems-Level Quality Improvement
  • Published:
Journal of Medical Systems Aims and scope Submit manuscript

Abstract

Traditional fecal erythrocyte detection is performed via a manual operation that is unsuitable because it depends significantly on the expertise of individual inspectors. To recognize human erythrocytes automatically and precisely, automatic segmentation is very important for extraction of characteristics. In addition, multiple recognition algorithms are also essential. This paper proposes an algorithm based on morphological segmentation and a fuzzy neural network. The morphological segmentation process comprises three operational steps: top-hat transformation, Otsu’s method, and image binarization. Following initial screening by area and circularity, fuzzy c-means clustering and the neural network algorithms are used for secondary screening. Subsequently, the erythrocytes are screened by combining the results of five images obtained at different focal lengths. Experimental results show that even when the illumination, noise pollution, and position of the erythrocytes are different, they are all segmented and labeled accurately by the proposed method. Thus, the proposed method is robust even in images with significant amounts of noise.

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. Berkson, J., Magath, T. B., and Hurn, M., Laboratory standards in relation to chance fluctuations of the erythrocyte count as estimated with the hemocytometer. J. Am. Stat. Assoc. 30:416–426, 1935. doi:10.1080/01621459.1935.10502287.

    Article  Google Scholar 

  2. Dein, F. J., Wilson, A., Fischer, D., and Langenberg, P., Avian leucocyte counting using the hemocytometer. J. Zoo. Wildl. Med. 25(3):432–437, 1994.

    Google Scholar 

  3. Fulwyler, M. J., Electronic separation of biological cells by volume. Science 150(3698):910–911, 1965.

    Article  CAS  PubMed  Google Scholar 

  4. Petersen, S. E., Bichel, P., and Lorentzen, M., Flow-cytometric demonstration of tumour-cell subpopulations with different DNA content in human colo-rectal carcinoma. Eur. J. Cancer 15:383–386, 1979.

    Article  CAS  PubMed  Google Scholar 

  5. Zhang, J., Lin, Y., Liu, Y., Li, Z., Li, Z., Hu, S., Liu, Z., Lin, D., and Wu, Z., Cascaded-automatic segmentation for schistosoma japonicum eggs in images of fecal samples. Comput. Biol. Med. 52:18–27, 2014.

    Article  CAS  PubMed  Google Scholar 

  6. Yang, Y. S., Park, D. K., Kim, H. C., Choi, M.-H., and Chai, J.-Y., Automatic identification of human helminth eggs on microscopic fecal specimens using digital image processing and an artificial neural network. IEEE Trans. Biomed. Eng. 48(6):718–730, 2001.

    Article  CAS  PubMed  Google Scholar 

  7. Soille, P., Morphological image analysis-principle and applications (Springer, 2003).

  8. Madhloom, H. T., Kareem, S. A., and Ariffin, H., An image processing application for the localization and segmentation of lymphoblast cell using peripheral blood images. J. Med. Syst. 36(4):2149–2158, 2012.

    Article  PubMed  Google Scholar 

  9. Bai, X., Zhou, F., and Xue, B., Fusion of infrared and visual images through region extraction by using multi scale center surround top-hat transform. Opt. Express 19(9):8444–8457, 2011.

    Article  PubMed  Google Scholar 

  10. Zeng, M., Li, J., and Peng, Z., The design of Top-Hat morphological filter and application to infrared target detection. Infrared Phys. Technol. 48:67–76, 2006.

    Article  Google Scholar 

  11. Ostu, N., A Threshold selection method from gray-level histograms. IEEE. Trans. Syst. Man. Cybenet. SMC-9(1):62–66, 1979.

    Google Scholar 

  12. Sezgin, M., and Sankur, B., Survey over image threshold techniques and quantitative performance evaluation. J. Electron. Imag. 13(1):146–165, 2004.

    Article  Google Scholar 

  13. Avci, D., Leblebiciogdu, M. K., Poyraz M., Dogantekin, E., A new method based on adaptive discrete wavelet entropy energy and neural network classifier (ADWEENN) for recognition of urine cells from microscopic images independent of rotation and scaling. J. Med. Syst. 38(7):2014. doi: 10.1007/s10916-014-0007-3.

  14. Bezdek, J. C., Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York, 1981.

    Book  Google Scholar 

Download references

Acknowledgments

This research is supported partly by National Natural Science Foundation of China (61405028 and 61205004) and Central Universities (University of Electronic Science and Technology of China) Fundamental Research Funds (ZYGX2013J065) and the funds of Guangdong-HongKong Break through Bidding Project in Key Areas (20122205106 and 20091683).

I would like to express my gratitude to professor Yutang Ye and all those who have helped me during the writing of this thesis.

Author information

Authors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Lin Liu, Haoting Lei, Jing Zhang, Yang Yuan, Zhenglong Zhang, Juanxiu Liu, Yu Xie, Guangming Ni & Yong Liu

Authors
  1. Lin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haoting Lei
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yang Yuan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zhenglong Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Juanxiu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yu Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Guangming Ni
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Yong Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Haoting Lei.

Additional information

This article is part of the Topical Collection on Systems-Level Quality Improvement

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, L., Lei, H., Zhang, J. et al. Automatic Identification of Human Erythrocytes in Microscopic Fecal Specimens. J Med Syst 39, 146 (2015). https://doi.org/10.1007/s10916-015-0334-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10916-015-0334-z

Keywords

Search

Navigation