Machine Vision and Applications

, Volume 22, Issue 3, pp 461–469 | Cite as

Robust contour reconstruction of red blood cells and parasites in the automated identification of the stages of malarial infection

  • Saravana Kumar Kumarasamy
  • S. H. Ong
  • K. S. W. Tan
Original Paper

Abstract

We present a novel method for detecting malaria parasites and determining the stage of infection from digital images comprising red blood cells (RBCs). The proposed method is robust under varying conditions of image luminance, contrast and clumping of RBCs. Both strong and weak boundary edges of the RBCs and parasites are detected based on the similarity measure between local image neighborhoods and predefined edge filters. A rule-based algorithm is applied to link edge fragments to form closed contours of the RBCs and parasite regions, as well as to split clumps into constituent cells. A radial basis support vector machine determines the stage of infection from features extracted from each parasite region. The proposed method achieves 97% accuracy in cell segmentation and 86% accuracy in parasite detection when tested on a total of 530 digitally captured images of three species of malaria parasites: Plasmodium falciparum, Plasmodium yoelii and Plasmodium berghei.

Keywords

Edge detection Gestalt laws Malaria Segmentation Support vector machine 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Ang H.H., Lam C.K., Wah M.J.: In vitro susceptibility studies of Plasmodium falciparum isolates and clones against type ii antifolate drugs. Chemotherapy 42, 318–323 (1996)CrossRefGoogle Scholar
  2. 2.
    Bloland, P.B.: Drug resistance in malaria. Technical Report WHO/CDS/CSR/DRS/ 2001.4., World Health OrganizationGoogle Scholar
  3. 3.
    Boring E.G.: Sensation and perception in the history of experimental psychology. J. Philos. 41, 334–335 (1944)CrossRefGoogle Scholar
  4. 4.
    Donoho D.L., Johnstone I.M.: Adapting to unknown smoothness via wavelet shrinkage. J. Am. Stat. Assoc. 90, 1200–1224 (1995)CrossRefMATHMathSciNetGoogle Scholar
  5. 5.
    Fitzgibbon A.W., Pilu M., Fisher R.B.: Direct least square fitting of ellipses. IEEE Trans. Pattern Anal. Mach. Intell. 21, 476–480 (1999)CrossRefGoogle Scholar
  6. 6.
    Forero M.G., Sroubek F., Cristóbal G.: Identification of tuberculosis bacteria based on shape and color. Real Time Imaging 10, 251–262 (2004)CrossRefGoogle Scholar
  7. 7.
    Frean J.A.: Reliable enumeration of malaria parasites in thick blood films using digital image analysis. Malaria J. 8, 218 (2009)CrossRefGoogle Scholar
  8. 8.
    Friedman, F.: Another approach to polychotomous classification. Technical report, Stanford University (1996)Google Scholar
  9. 9.
    Gonzales R.C., Woods R.E.: Digital Image Processing. Prentice-Hall, New Jersey (2002)Google Scholar
  10. 10.
    Haykin S.: Neural Networks—A Comprehensive Foundation. Prentice-Hall, New Jersey (1999)MATHGoogle Scholar
  11. 11.
    Jean-Philippe T., Benoît M.: Morphological feature extraction for the classification of digital images of cancerous tissues. IEEE Trans. Biomed. Eng. 43, 1011–1020 (1996)CrossRefGoogle Scholar
  12. 12.
    Jiangwen D., Tsui H.T.: A fast level set method for segmentation of low contrast noisy biomedical images. Pattern Recognit. Lett. 23, 161–169 (2002)CrossRefMATHGoogle Scholar
  13. 13.
    Kumar S., Ong S.H., Ranganath S., Chew F.T.: A luminance- and contrast-invariant edge-similarity measure. IEEE Trans. Pattern Anal. Mach. Intell. 28, 2042–2048 (2006)CrossRefGoogle Scholar
  14. 14.
    Kumar S., Ong S.H., Ranganath S., Ong T.C., Chew F.T.: A rule-based approach for robust clump splitting. Pattern Recognit. 39, 1088–1098 (2006)CrossRefMATHGoogle Scholar
  15. 15.
    Le M.T., Bretschneider T.R., Kuss C., Preiser P.R.: A novel semi-automatic image processing approach to determine Plasmodium falciparum parasitemia in Giemsa-stained thin blood smears. BMC Cell Biol. 9, 15 (2008)CrossRefGoogle Scholar
  16. 16.
    Makler M.T., Palmer C.J., Alger A.L.: A review of practical techniques for the diagnosis of malaria. Ann. Trop. Med. Parasitol. 92, 419–433 (1998)CrossRefGoogle Scholar
  17. 17.
    Pammenter M.D.: Techniques for the diagnosis of malaria. S. Afr. Med. J. 74, 55–57 (1988)Google Scholar
  18. 18.
    Pratt W.K.: Digital Image Processing. Wiley, New York (1991)MATHGoogle Scholar
  19. 19.
    Ross N.E., Pritchard C.J., Rubin D.M., Dusé A.G.: Automated image processing method for the diagnosis and classification of malaria on thin blood smears. Med. Biol. Eng. Comput. 44, 427–436 (2006)CrossRefGoogle Scholar
  20. 20.
    Ruberto C.D., Dempster A., Khan S., Jarra B.: Analysis of infected blood cell images using morphological operators. Image Vis. Comput. 20, 133–146 (2002)CrossRefGoogle Scholar
  21. 21.
    Smith T.G., Lourenco P., Carter R., Walliker D., Ranford-Cartwright L.C.: Commitment to sexual differentiation in the human malaria parasite, Plasmodium falciparum. Parasitology 121, 127–133 (2000)CrossRefGoogle Scholar
  22. 22.
    Tatsumi N., Pierre R.V.: Automated image processing—past, present and future of blood cell morphology identification. Clin. Lab. Med. 22, 299–315 (2002)CrossRefGoogle Scholar
  23. 23.
    Tek F.B., Dempster A.G., Kale I.: Parasite detection and identification for automated thin blood film malaria diagnosis. Comput. Vis. Image Underst. 114, 21–32 (2010)CrossRefGoogle Scholar
  24. 24.
    Trager W., Jensen J.B.: Human malaria parasites in continuous culture. Chemotherapy 193, 673–675 (1976)Google Scholar
  25. 25.
    Walliker D., Quakyi I.A., Wellems T.E.: Genetic analysis of the human malaria parasite Plasmodium falciparum. Science 236, 1661–1666 (1987)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Saravana Kumar Kumarasamy
    • 1
  • S. H. Ong
    • 2
  • K. S. W. Tan
    • 3
  1. 1.Institute of Bioengineering and NanotechnologySingaporeSingapore
  2. 2.Division of Bioengineering, Department of Electrical and Computer EngineeringNational University of SingaporeSingaporeSingapore
  3. 3.Department of MicrobiologyNational University of SingaporeSingaporeSingapore

Personalised recommendations