Advertisement

Diagnosis of Malaria Using Wavelet Coefficients and Dynamic Time Warping

  • Purnima PanditEmail author
  • A. Anand
Original Paper

Abstract

Malaria remains one of the world’s most deadly infectious disease and arguably, the greatest menace to modern society in terms of morbidity and mortality. Demand of time is to identify the methods, which can diagnose the disease and are economical and accurate. In this work, we have proposed a technique based on scientific computations. The aim is to provide a method which can do classification of Red Blood Cells (RBCs) into the malaria infected and healthy ones. The images of RBCs are captured using low cost digital holographic interferometric microscope (DHIM), these images are pre-processed for noise removal and then used for computing the discrete wavelet coefficients (DWC). The DWC are then used, as feature vectors, in dynamic time warping algorithm to find similarities or dissimilarities, between the RBCs. This allows the classification of RBCs into healthy or malaria infected. The proposed method shows excellent results for classification of RBCs for diagnosis of disease. Such automated detection methods, using low cost DHIM devices and minimal human intervene, will be good aid to medical area and are demand of time.

Keywords

Malaria disease Discrete wavelet coefficients Dynamic time warping Digital holographic microscopic images 

Notes

References

  1. 1.
    Moody, A.: Rapid diagnostic tests for malaria parasites. Clin. Microbiol. Rev. 15(1), 66–78 (2002)CrossRefGoogle Scholar
  2. 2.
    Diaz, G., Gonzalez, F., Romero, E.: Infected cell identification in thin blood images based on color pixel classification: comparison and analysis. Lect. Notes Comput. Sci. 4756, 812–821 (2007)CrossRefGoogle Scholar
  3. 3.
    Anand, A., Chhaniwal, V.K., Patel, N.R., Javidi, B.: Automatic identification of malaria infected RBC with digital holographic microscope using correlation algorithm. IEEE Photonic J. 4(5), 1456–1464 (2012)CrossRefGoogle Scholar
  4. 4.
    Rosadoa, L., da Costa, J.M.C., Elias, D., Cardoso, J.S.: Automated detection of malaria on thick blood smears via mobile devices. Procedia Comput. Sci. 90, 138–144 (2016)CrossRefGoogle Scholar
  5. 5.
    Khan, N.A., Pervaz, H., Latif, A., Musharaff, A.: Unsupervised identification of malaria parasites using computer vision. Pak. J. Pharm. Sci. 30(1), 223–227 (2017)Google Scholar
  6. 6.
    Krampa, F.D., Aniweh, Y., Awandare, G.A., Kanyong, P.: Recent progress in the development of diagnostic tests for malaria. Diagnostics (Basel, Switzerland) 7(3), 54 (2017)Google Scholar
  7. 7.
    Devi, S.S., Roy, A., Singha, J., Sheikh, S.A., Laskar, R.H.: Malaria infected erythrocyte classification based on a hybrid classifier using microscopic images of thin blood smear. Multimed. Tools Appl. 77(1), 631–660 (2018)CrossRefGoogle Scholar
  8. 8.
    Agbana, T.E., Diehl, J.C., Pul, F., Khan, S.M., Patlan, V., Verhaegen, M., Vdovin, G.: Imaging and identification of malaria parasites using cellphone microscope with a ball lens. PLoS ONE 13(10), e0205020 (2018)CrossRefGoogle Scholar
  9. 9.
    Pham, N.M., Karlen, W., Beck, H.P., Delamarche, E.: Malaria and the ‘last’ parasite: how can technology help? Malar. J. 17, 260 (2018)CrossRefGoogle Scholar
  10. 10.
    Schnars, U., Jüptner, W.: Digital recording and numerical reconstruction of holograms. Meas. Sci. Technol. 13, R85–R101 (2002)CrossRefGoogle Scholar
  11. 11.
    Pandit, P., Anand, A.: Artificial Neural Networks for Detection of Malaria in RBCs. arXiv:1608.06627
  12. 12.
    Charles, C.K.: An Introduction to Wavelets, vol. 1. Academic press, Cambridge (2014)Google Scholar
  13. 13.
    Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)CrossRefGoogle Scholar
  14. 14.
    Keogh, E., Ratanamahatana, C.A.: Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7(3), 358–386 (2005)CrossRefGoogle Scholar

Copyright information

© Springer Nature India Private Limited 2019

Authors and Affiliations

  1. 1.Department of Applied MathematicsThe Maharaja Sayajirao University of BarodaVadodaraIndia
  2. 2.Department of Applied PhysicsThe Maharaja Sayajirao University of BarodaVadodaraIndia

Personalised recommendations