Determination of Plasmodium Parasite Life Stages and Species in Images of Thin Blood Smears Using Artificial Neural Network

DOI: 10.4236/ojcd.2014.42014   PDF   HTML   XML   3,089 Downloads   4,150 Views   Citations

Abstract

Malaria is a leading cause of deaths globally. Rapid and accurate diagnosis of the disease is key to its effective treatment and management. Identification of plasmodium parasites life stages and species forms part of the diagnosis. In this study, a technique for identifying the parasites life stages and species using microscopic images of thin blood smears stained with Giemsa was developed. The technique entailed designing and training Artificial Neural Network (ANN) classifiers to perform the classification of infected erythrocytes into their respective stages and species. The outputs of the system were compared to the results of expert microscopists. A total of 205 infected erythrocytes images were used to train and test the performance of the system. The system recorded 99.9% in recognizing stages and 96.2% in recognizing plasmodium species.

Share and Cite:

Gitonga, L. , Maitethia Memeu, D. , Amiga Kaduki, K. , Allen Christopher Kale, M. and Samson Muriuki, N. (2014) Determination of Plasmodium Parasite Life Stages and Species in Images of Thin Blood Smears Using Artificial Neural Network. Open Journal of Clinical Diagnostics, 4, 78-88. doi: 10.4236/ojcd.2014.42014.

Conflicts of Interest

The authors declare no conflicts of interest.

References

[1] Korenromp, E., Miller, J., Nahlen, B., Wardlaw, T. and Young, M. (2005) World Malaria Report 2005. Tech Rep World Health Organisation, Geneva.
[2] Foster, S. and Philip, M. (1998) Economics and Its Contribution to the Fight against Malaria. Annals of Tropical Medicine and Parasitology, 92, 391-398.
http://dx.doi.org/10.1080/00034989859375
[3] Hay, S.I., Guerra, C.A., Tatem, A.J., Noor, A.M. and Snow, R.W. (2004) The Global Distribution and Population at Risk of Malaria: Past, Present, and Future. The Lancet Infectious Diseases, 4, 327-336.
http://dx.doi.org/10.1016/S1473-3099(04)01043-6
[4] Zhou, M., Liu, Q., Wongsrichanalai, C., Suwonkerd, W., Panart, K., Prajakwong, S., Pensir, A., Kimura, M., Atsuoka, H., Ferreira, M.U., Isomura, S. and Kawamoto, F. (1998) High Prevalence of Plasmodium malariae and Plasmodium ovale in Malaria Patients along the Thai-Myanmar Border, as Revealed by Acridine Orange Staining and PCR-Based Diagnoses. Tropical Medicine and International Health.
[5] Robert, V. and Boudin, C. (2003) Biology of Man-Mosquito Plasmodium Transmission. Bulletin de la Société de Pa- thologie Exotique, 96, 6-20.
[6] WHO (2010) Guidelines for the Treatment of Malaria. 2nd Edition.
[7] Warhurst, D.C. and Williams, J.E. (1996) Laboratory Diagnosis of Malaria. ACO Broadsheet No.148, Journal of Clinical Pathology, 49, 533-538.
http://dx.doi.org/10.1136/jcp.49.7.533
[8] Kawamoto, F., Miyake, H., Kaneko, O., Kimura, M., Dung, Liu, Q., Zhou, M., Duc Dao, L., Kawai, S., Isomura, S. and Wataya, Y. (1996) Sequence Variation in the 18sRNA Gene, a Target for PCR-Based Malaria Diagnosis in Plasmodium Ovale from Southern Vietnam. Journal of Clinical Microbiology, 34, 2287-2289.
[9] Seesod, N., Nopparar, P., Hedrum, A., Holder, A., Thiathong, S., Uhlen, M. and Lundeburg, J. (1997) An Integrated System Using Immune-Magnetic Separation, Polymerase Chain Reaction, and Colorimetric Detection for Diagnosis of Plasmodium Falciparum. The American Journal of Tropical Medicine and Hygiene, 56, 322-328.
[10] Beadle, C., Long, G.W., Weiss, W.R., McElroy, P.D., Maret, S.M., Oloo, A.J. and Hoffman, S.L. (1994) Diagnosis of Malaria by Detection of p. Falciparum HRP-2 Antigen with a Rapid Dipstick Antigen—Capture Assay. Lancet, 343, 564-568.
http://dx.doi.org/10.1016/S0140-6736(94)91520-2
[11] Report of Joint WHO/USAID (2005) New Perspectives of Malaria Diagnostics. Informal Consultations, 25-27 October 1999.
[12] Mirdha, B., Samantaray, S. and Mishra, B. (1997) Laboratory Diagnosis of Malaria. Journal of Clinical Pathology.
http://dx.doi.org/10.1136/jcp.50.4.356-a
[13] Kain, K.C., Harrington, M.A., Tennyson, S. and Keystone, J.S. (1998) Imported Malaria; Prospective Analysis of Problems in Diagnosis and Management. Clinical Infectious Diseases, 27, 142-149.
http://dx.doi.org/10.1086/514616
[14] Witkowski, B., Lelièvre, J., Barragán, M.J., et al. (2010) Increased Tolerance to Artemisinin in Plasmodium Falciparum Is Mediated by a Quiescence Mechanism. Antimicrobial Agents and Chemotherapy, 54, 1872-1877.
http://dx.doi.org/10.1128/AAC.01636-09
[15] Thapar, M.M., Gil, J.P. and Bjorkman, A. (2005) In Vitro Recrudescence of Plasmodium falciparum Parasites Suppressed to Dormant State by Atovaquone Alone and in Combination with Proguanil. Transactions of the Royal Society of Tropical Medicine and Hygiene, 99, 62-70.
[16] Ross, N.E., Pritchard, C.J., Rubin, D.M. and Duse, A.G. (2006) Automated Image Processing Method for the Diagnosis and Classification of Malaria on Thin Blood Smears. Medical & Biological Engineering & Computing, 44, 427-436.
http://dx.doi.org/10.1007/s11517-006-0044-2
[17] Di Ruberto, R.C., Dempster, A., Khan, S. and Jarra, B. (2002) Analysis of Infected Blood Cell Images Using Morphological Operators. Image and Vision Computing, 20, 133-146.
[18] Diaz, G., Gonzalez, F.A. and Eduardo, R. (2009) A Semi-Automatic Method for Quantification and Classification of Erythrocytes Infected with Malaria Parasites in Microscopic Images. Journal of Biomedical Informatics, 42, 296-307.
http://dx.doi.org/10.1016/j.jbi.2008.11.005
[19] Tek, F.B., Dempster, A.G. and Kale, I. (2009) Computer Vision for Microscopy Diagnosis of Malaria. Malaria Journal, 8, 153. http://dx.doi.org/10.1186/1475-2875-8-153
[20] Raj, K.B., Ashok, K.G. and Manoj, K.S. (2009) Matlab and Its Application in Engineering.
[21] Hagan, M.T., Demuth, H.B. and Beale, M. (1996) Neural Network Design. Pws Publishing Company.
[22] http://www.dpd.cdc.gov/dpdx/HTML/Frames/M-R/Malaria/body_Malariadiagfind2.htm
[23] http://www.kemri.org/
[24] Daniel, M.M. (2013) A Rapid Malaria Diagnostic Method Based on Automatic Detection and Classification of Plasmodium Parasites in Stained Thin Blood Smear Images. MSc Thesis, University of Nairobi.

  
comments powered by Disqus

Copyright © 2020 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.