Color Features Extraction and Classification of Digital Images of Erythrocytes Infected by Plasmodium berghei

  • Juan V. Lorenzo-GinoriEmail author
  • Lyanett Chinea-Valdés
  • Yanela IzquierdoTorres
  • Rubén Orozco-Morales
  • Niurka Mollineda-Diogo
  • Sergio Sifontes-Rodríguez
  • Alfredo Meneses-Marcel
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)


The development of antimalarial drugs requires performing laboratory experiments that include the analysis of blood smears infected with Plasmodium berghei. Analyzing visually the resulting microscopy images is usually a slow and tedious task prone to errors due to fatigue and subjectivity of the analysts. These facts motivated the creation of digital image processing systems to automate the aforementioned analysis. We present in this work a computer vision solution which processes microscopy images of blood smears. This system performs tasks like illumination correction, color compensation, image segmentation including separation of clumped objects and the extraction and selection of color features. Then a set of classifiers was tested to find the best one in terms of classification results. Here a new feature named pixels fraction was introduced and a number of other color features were extracted, from which a subset was selected for the classification of the cells into either normal or infected. The classifiers tested for this application were: support vector machines (SVM), K-nearest neighbors (KNN), J48, Random Forest (RF), Naïve Bayes and linear discriminant analysis (LDA). All of them were evaluated in terms of their performance expressed as correct classification rate, sensitivity, specificity, F-measure and area under Receiver Operating Characteristic (ROC) curve (AUC). The usefulness of the pixels fraction as a new and effective feature was demonstrated by the experimental results. In regard of classifiers, J48 and Random Forest showed the best results.


Malaria Image processing Computer vision Feature extraction Classifiers 



The authors acknowledge the VLIR-UOS Project Cuba ICT Network for the financial support provided to this work.


  1. 1.
    World Health Organization. World Malaria Report (2017)Google Scholar
  2. 2.
    Arco, J.E., Górriz, J.M., Ramírez, J., et al.: Digital image analysis for automatic enumeration of malaria parasites using morphological operations. Expert Syst. Appl. 42, 3041–3047 (2015). Scholar
  3. 3.
    Abdul-Nasir, A.S., Mashor, M.Y., Mohamed, Z.: Colour image segmentation approach for detection of malaria parasites using various colour models and k-means clustering. WSEAS Trans. Biol. Biomed. 10(1), 41–55 (2013)Google Scholar
  4. 4.
    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). Scholar
  5. 5.
    Das, D.K., Maiti, A.K., Chakraborty, C.: Automated system for characterization and classification of malaria-infected stages using light microscopic images of thin blood smears. J. Microsc. 257, 238–252 (2015). Scholar
  6. 6.
    Di Ruberto, C., Dempster, A., Khan, S., Jarra, B.: Analysis of infected blood cell images using morphological operators. Image Vis. Comput. 20, 133–146 (2002). Scholar
  7. 7.
    Ajala, F.: Comparative analysis of different types of malaria diseases using first order features. Int. J. Appl. 8, 20–26 (2015). Scholar
  8. 8.
    Loddo, A., Di Ruberto, C., Kocher, M.: Recent advances of malaria parasites detection systems based on mathematical morphology. Sensors 18(2), 513 (2018). Scholar
  9. 9.
    Chavan, S., Nagmode, M.: Malaria disease identification and analysis using image processing. Int. J. Latest Trends Eng. Technol. 3(3), 218–223 (2014)Google Scholar
  10. 10.
    Walpole, R.E., Myers, R.H., Myers, S.L., Keying, E.Y.: Probability and Statistics for Engineers and Scientists: Pearson New International Edition. Pearson Higher Education, Upper Saddle River (2013)zbMATHGoogle Scholar
  11. 11.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Pearson Prentice Hall, Upper Saddle River (2008)Google Scholar
  12. 12.
    Jierong, C., Rajapakse, J.C.: Segmentation of clustered nuclei with shape markers and marking function. IEEE Trans. Biomed. Eng. 56(3), 741–748 (2009). Scholar
  13. 13.
    Saikrishna, T.V., Yesubabu, A., Anandarao, A., Rani, T.S.: A novel image retrieval method using segmentation and color moments. Adv. Comput. 3(1), 75–80 (2012). Scholar
  14. 14.
    Bouckaert, R., Frank, E., Hall, M., et al.: WEKA Manual for Version 3-6-13. CreateSpace Independent Publishing Platform (2015)Google Scholar

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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Universidad Central Marta Abreu de Las VillasSanta ClaraCuba
  2. 2.Centro de Bioactivos QuímicosSanta ClaraCuba

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