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.
Similar content being viewed by others
References
Moody, A.: Rapid diagnostic tests for malaria parasites. Clin. Microbiol. Rev. 15(1), 66–78 (2002)
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)
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)
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)
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)
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)
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)
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)
Pham, N.M., Karlen, W., Beck, H.P., Delamarche, E.: Malaria and the ‘last’ parasite: how can technology help? Malar. J. 17, 260 (2018)
Schnars, U., Jüptner, W.: Digital recording and numerical reconstruction of holograms. Meas. Sci. Technol. 13, R85–R101 (2002)
Pandit, P., Anand, A.: Artificial Neural Networks for Detection of Malaria in RBCs. arXiv:1608.06627
Charles, C.K.: An Introduction to Wavelets, vol. 1. Academic press, Cambridge (2014)
Mallat, S.G.: A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans. Pattern Anal. Mach. Intell. 11(7), 674–693 (1989)
Keogh, E., Ratanamahatana, C.A.: Exact indexing of dynamic time warping. Knowl. Inf. Syst. 7(3), 358–386 (2005)
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Pandit, P., Anand, A. Diagnosis of Malaria Using Wavelet Coefficients and Dynamic Time Warping. Int. J. Appl. Comput. Math 5, 26 (2019). https://doi.org/10.1007/s40819-019-0614-2
Published:
DOI: https://doi.org/10.1007/s40819-019-0614-2