Detection of Plasmodium Falciparum in Peripheral Blood Smear Images

  • Feminna Sheeba
  • Robinson Thamburaj
  • Joy John Mammen
  • Atulya K. Nagar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 202)


Malaria is a mosquito-borne infectious disease caused by the parasite Plasmodium, which requires accurate and early diagnosis for effective containment. In order to diagnose malaria in a patient, timely detection of malaria parasites in blood smear images is vital. The traditional methods are time–consuming, tedious and the quality of detection is highly subjective to the individual who performs the analysis. These results can clearly be improved upon by using image processing techniques. The malaria parasite appears in four stages, namely the ring, trophozoite, schizont, and gametocyte. The ring and the gametocyte stage are the ones seen in a peripheral blood smear and hence detecting these two stages, would help in the accurate diagnosis of malaria. The proposed work aims at automating the analysis of the blood smear images using appropriate segmentation techniques, thereby detecting infected red blood cells as well as the gametocytes found in the blood.


Plasmodium Chromatin dots Gametocytes Segmentation Morphology 


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The authors would like to thank the Centre for Applicable Mathematics and Systems Science (CAMSS), Department of Computer Science, Liverpool Hope University, UK for the support and funding towards this project work and the Department of Pathology, CMC, Vellore, India for providing them with sample images for the study. The authors also thank Miss Maqlin P. and Dickson Jebaraj, Madras Christian College, Chennai, for their contribution towards the development of the system that performs automatic segmentation of malaria parasites in blood smear images.


  1. Sheeba, F., Thomas, Hannah, M.T.T., Mammen. J.J.: Segmentation and Reversible Watermarking of Peripheral Blood Smear Images. Proc. of the IEEE Fifth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), 1373-6.IEEE BIC-TA (2010).Google Scholar
  2. Sheeba, F., Thamburaj. R,, J.J. Mammen, Hannah.M.T.T., Nagar,A.K.: White Blood Cell Segmentation and Watermarking. Proc. of the IASTED International Symposia Imaging and Signal Processing in Healthcare and Technology, Washington DC, USA. ISPHT (2011)Google Scholar
  3. Sheeba, F., Thamburaj, R., Nagar, A.K., Mammen, J.J.: Segmentation of Peripheral Blood Smear Images using Tissue-like P Systems. The 6th International Conference on Bio-Inspired Computing pp. 257-261. BICTA (2011).Google Scholar
  4. Anggraini, D., Nugroho, A.S., Pratama, C., Rozi, I.E., Iskandar, A.A., Hartono, R.N.: Automated Status Identification of Microscopic Images Obtained from Malaria Thin Blood Smears. International Conference on Electrical Engineering and Informatics. 1-6. ICEEI (2011).Google Scholar
  5. Selena W.S. et al, Malaria Count.: An image analysis-based program for the accurate determination of parasitemia. Journal of Microbiological Methods. Elsevier (2006). doi: 10.1016/j.mimet.2006.05.017
  6. Tek, F.B., Dempsterb, A.G., Kale, I.: Computer Vision for Microscopy Diagnosis of Malaria. Malaria Journal. 8:153. 2009. doi: 10.1186/1475-2875-8-153 Google Scholar
  7. Tek, F.B., Dempsterb, A.G., Kale, I.: Malaria Parasite Detection in Peripheral Blood Images. In: British Machine Vision Conference 347-356. BMVC (2006).Google Scholar
  8. Premaratne, S.P., Karunaweera, N.D., Fernando, S., Perera, W.S.R., Rajapaksha, R.P.A. : A Neural Network Architecture for Automated Recognition of Intracellular Malaria Parasites in Stained Blood Films,
  9. Hirimutugoda,Y.M, Wijayarathna, G. : Image Analysis System for Detection of Red Cell Disorders Using Artificial Neural Networks, Journal of Bio-Medical Informatics. 1(1): 35-42. (2010)Google Scholar
  10. Halim, S., Bretschneider, T.R., Yikun Li., Preiser, P.R., Kuss, C.: Estimating Malaria Parasitaemia from Blood Smear Images. 9th International Conference on Control, Automation, Robotics and Vision. 1-6. ICARCV (2006).Google Scholar
  11. Makkapati, V.V., Rao R.M..: Segmentation of Malaria Parasites in Peripheral Blood Smear Images. In: IEEE Conf. on Acoustics, Speech and Signal Processing 1361-1364. ICASSP (2009).Google Scholar
  12. Gonzalez, R.C., Woods, R.E.: Digital Image Processing 3ed. by Prentice Hall. (eds). (2008).Google Scholar
  13. Gonzalez, R.C., Woods, R.E., Eddins, S.L.: Digital Image Processing using Matlab. ed, by Pearson Education.(2009).Google Scholar

Copyright information

© Springer India 2013

Authors and Affiliations

  • Feminna Sheeba
    • 1
  • Robinson Thamburaj
    • 2
  • Joy John Mammen
    • 3
  • Atulya K. Nagar
    • 4
  1. 1.Department of Computer ScienceMadras Christian CollegeChennaiIndia
  2. 2.Department of MathematicsMadras Christian CollegeChennaiIndia
  3. 3.Department of Transfusion Medicine and ImmunohaematologyChristian Medical CollegeVelloreIndia
  4. 4.Centre for Applicable Mathematics and Systems Science, Department of Computer ScienceLiverpool Hope UniversityLiverpoolUK

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