Infected Cell Identification in Thin Blood Images Based on Color Pixel Classification: Comparison and Analysis

  • Gloria Díaz
  • Fabio Gonzalez
  • Eduardo Romero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4756)


Malaria is an infectious disease which is mainly diagnosed by visual microscopical evaluation of Giemsa-stained thin blood films using a differential analysis of color features. This paper presents the evaluation of a color segmentation technique, based on standard supervised classification algorithms. The whole approach uses a general purpose classifier, which is parameterized and adapted to the problem of separating image pixels into three different classes: parasite, blood red cells and background. Assessment included not only four different supervised classification techniques - KNN, Naive Bayes, SVM and MLP - but different color spaces -RGB, normalized RGB, HSV and YCbCr-. Results show better performance for the KNN classifiers along with an improving feature characterization in the normalized RGB color space.


Cell detection Supervised classification Color spaces Performance comparison 


  1. 1.
    WMR, UNICEF: World malaria report. Technical report, WMR and UNICEF (2005)Google Scholar
  2. 2.
    OPS: The health in the americas. Technical Report 1, Pan-american organization of the Health (1998)Google Scholar
  3. 3.
    di Ruberto, C., Dempster, A., Khan, S., Jarra, B.: Analysis of infected blood cell images using morphological operators. Image and Vision Computing 20(2), 133–146 (2002)CrossRefGoogle Scholar
  4. 4.
    Tek, F., Dempster, A., Kale, I.: Malaria parasite detection in peripheral blood images. In: Proceeding of British Machine Vision Conference (2006)Google Scholar
  5. 5.
    Halim, S., Bretschneider, T.R., Li, Y., Preiser, P.R., Kuss, C.: Estimating malaria parasitaemia from blood smear images. In: Proceedings of the IEEE International Conference on Control, Automation, Robotics and Vision (2006)Google Scholar
  6. 6.
    Ross, N.E., Pritchard, C.J., Rubin, D.M., Dus, A.G.: Automated image processing method for the diagnosis and classification of malaria on thin blood smears. Medical and Biological Engineering and Computing 44, 427–436 (2006)CrossRefGoogle Scholar
  7. 7.
    Sio, S.W.S., Sun, W., Kumar, S., Bin, W.Z., Tan, S.S., Ong, S.H., Kikuchi, H., Oshima, Y., Tan, K.S.W.: Malariacount: an image analysis-based program for the accurate determination of parasitemia. Microbiological Methods 68 (2007)Google Scholar
  8. 8.
    di Stefano, L., Bulgarelli, A.: A simple and efficient connected components labeling algorithm. In: Proceedings of the 10th International Conference on Image Analysis and Processing (1999)Google Scholar
  9. 9.
    Wolpert, D., Macready, W.: No free lunch theorems for optimization. IEEE Transactions on Evolutionary Computation 1, 67–82 (1997)CrossRefGoogle Scholar
  10. 10.
    Cover, T., Hart, P.: Nearest neighbor pattern classification. IEEE Transactions on Information Theory IT-13, 21–27 (1967)CrossRefGoogle Scholar
  11. 11.
    Fix, E., Hodges, J.: Discriminatory analysis, non-parametric discrimination. Technical Report Project 21-49-004, Rept. 4, Contract AF41(128)-131, USAF School of Aviation Medicine, Randolf Field, Texas (February 1951)Google Scholar
  12. 12.
    Rumelhart, D., Hinton, G., Williams, R.: Parallel Distributed Processing: Explorations in Macrostructure of cognition, vol. I. Badford Books, Cambridge. MA (1986)Google Scholar
  13. 13.
    Platt, J.: Machines using sequential minimal optimization. In: Schoelkopf, B., Burges, C., Smola, A. (eds.) Advances in Kernel Methods - Support Vector Learning, MIT Press, Cambridge (1998)Google Scholar
  14. 14.
    Daskalaki, S., Kopanas, I., Avouris, N.: Evaluation of classifiers for an uneven class distribution problem. Applied Artificial Intelligence 20, 381–417 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • Gloria Díaz
    • 1
  • Fabio Gonzalez
    • 1
  • Eduardo Romero
    • 1
  1. 1.Bioingenium Research Group, National University of Colombia, BogotáColombia

Personalised recommendations