Skip to main content

Advertisement

SpringerLink
Log in

Automated image processing method for the diagnosis and classification of malaria on thin blood smears

  • ORIGINAL ARTICLE
  • Published:
Medical and Biological Engineering and Computing Aims and scope Submit manuscript

Abstract

Malaria is a serious global health problem, and rapid, accurate diagnosis is required to control the disease. An image processing algorithm to automate the diagnosis of malaria on thin blood smears is developed. The image classification system is designed to positively identify malaria parasites present in thin blood smears, and differentiate the species of malaria. Images are acquired using a charge-coupled device camera connected to a light microscope. Morphological and novel threshold selection techniques are used to identify erythrocytes (red blood cells) and possible parasites present on microscopic slides. Image features based on colour, texture and the geometry of the cells and parasites are generated, as well as features that make use of a priori knowledge of the classification problem and mimic features used by human technicians. A two-stage tree classifier using backpropogation feedforward neural networks distinguishes between true and false positives, and then diagnoses the species (Plasmodium falciparum, P. vivax, P. ovale or P. malariae) of the infection. Malaria samples obtained from the Department of Clinical Microbiology and Infectious Diseases at the University of the Witwatersrand Medical School are used for training and testing of the system. Infected erythrocytes are positively identified with a sensitivity of 85% and a positive predictive value (PPV) of 81%, which makes the method highly sensitive at diagnosing a complete sample provided many views are analysed. Species were correctly determined for 11 out of 15 samples.

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
Fig. 11

Similar content being viewed by others

References

  1. Bloland PB (2001) Drug resistance in malaria, WHO/CDS/CSR/DRS/ 2001.4. World Health Organization, Switzerland

    Google Scholar 

  2. Bruce-Chwatt LJ (1985) Essential malariology, 2nd edn. William Heinemann Medical Books, London

    Google Scholar 

  3. Di Ruberto C, Dempster A, Khan S, Jarra B (2002) Analysis of infected blood cell images using morphological operators. Image Vis Comput 20(2):133–146

    Article  Google Scholar 

  4. Foley DH (1972) Consideration of sample and feature size. IEEE Trans Inform Theory IT-18 618–626

    Google Scholar 

  5. Foster S, Phillips M (1998) Economics and its contribution to the fight against malaria. Ann Trop Med Parasitol 92:391–398

    Article  Google Scholar 

  6. Hibbard LS, McCasland JS, Brunstrom JE, Pearlman AL (1996) Automated recognition and mapping of immmunolabelled neurons in the developing brain. J Microsc 183(8):241–256

    Article  Google Scholar 

  7. Hughes GF (1968) On the mean accuracy of statistical pattern recognition. IEEE Trans Inform Theory IT-14, 55–63

    Google Scholar 

  8. Makler MT, Palmer CJ, Alger AL (1998) A review of practical techniques for the diagnosis of malaria. Ann Trop Med Parasitol 92(4):419–433

    Article  Google Scholar 

  9. Mohana Rao KNR, Dempster AG (2001) Area-granulometry: an improved estimator of size distribution of image objects. Electron Lett 37(15):950–951

    Article  Google Scholar 

  10. Mui JK, Fu K-S (1980) Automated classification of nucleated blood cells using a binary tree classifier. IEEE Trans Pattern Anal Machine Intell 2(5):429–443

    Google Scholar 

  11. Oaks SCJ, Mitchell VS, Pearson GW, Carpenter CCJ (eds) (1991) Malaria: obstacles and opportunities, National Academy Press, Washington, DC. A report of the committee for the study on malaria prevention and control: Status review and alternative strategies

  12. Ohrt C, Purnomo, Sutamihardja MA, Tang D, Kain KC (2002) Impact of microscopy error on estimates of protective efficacy in malaria-prevention trials. J Infect Dis 186(4):540–546

    Article  Google Scholar 

  13. Otsu N (1979) A threshold selection method from gray level histograms. IEEE Trans Syst Man Cybern SMC-9(1):62–66

    MathSciNet  Google Scholar 

  14. Pammenter MD (1988) Techniques for the diagnosis of malaria. S Afr Med J 74(2):55–57

    Google Scholar 

  15. Soni PN, Gouws E (1996) Severe and complicated malaria in KwaZulu-Natal. S Afr Med J 86(6):653–656

    Google Scholar 

  16. Theodoridis S, Koutroumbas K (1999) Pattern Recognition. Academic, San Diego

    Google Scholar 

  17. Thiran J-P, Macq B (1996) Morphological feature extraction for the classification of digitalim ages of cancerous tissues. IEEE Trans Biomed Eng 43(10):1011–1019

    Article  Google Scholar 

  18. World Health Organisation (1991) Basic malaria microscopy. World Health Organisation, Geneva

    Google Scholar 

Download references

Acknowledgements

The financial assistance of the Department of Labour (DoL) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at are those of the author and are not necessarily to be attributed to the DoL.

Author information

Authors and Affiliations

  1. Department of Engineering, Cambridge University, Trumpington Street, Cambridge, CB2 1PZ, UK

    Nicholas E. Ross

  2. School of Electrical and Information Engineering, University of the Witwatersrand, Private Bag 3, Johannesburg, Wits, 2050, South Africa

    Charles J. Pritchard & David M. Rubin

  3. Department of Clinical Microbiology and Infectious Diseases, University of the Witwatersrand Medical School, 7 York Road, Parktown, 2193, South Africa

    Adriano G. Dusé

Authors
  1. Nicholas E. Ross
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Charles J. Pritchard
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. David M. Rubin
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Adriano G. Dusé
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nicholas E. Ross.

Additional information

Nicholas E. Ross conducted research while at the University of the Witwatersrand School of Electrical and Information Engineering

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ross, N.E., Pritchard, C.J., Rubin, D.M. et al. Automated image processing method for the diagnosis and classification of malaria on thin blood smears. Med Bio Eng Comput 44, 427–436 (2006). https://doi.org/10.1007/s11517-006-0044-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11517-006-0044-2

Keywords

Search

Navigation