An Image Processing Approach for Accurate Determination of Parasitemia in Peripheral Blood Smear Images

Abstract

An interactive automatic procedure for detection of malaria from microscope blood images is presented. The user is required to select image from data set and the algorithm detects whether the blood is infected with malaria or not automatically. This method will help in reducing the time taken for diagnosis and the chance for human errors. A general framework to perform detection of malaria parasite, which includes an image pre-processing, extracting infected blood cells, morphological operation and highlighting the infected cells, is described. We have evaluated our algorithm using a dataset of 76 microscopic blood images from different patients (both infected and uninfected).Experimental results show that the proposed algorithm achieves 94. 87% sensitivity and 97. 3% specificity for the malaria parasite detection. This methodology may serve as a rapid diagnostic tool for malaria, even in microscopically negative cases. We also present open research problems.

Reference

• E. Korenromp, J. Miller , B. Nahlen , T. Wwardlaw, M.Young ,World Malaria report,2005,Technical Report, World Health organization,geneva,2005.
• World Malaria Report 2008, World Health organization ISBN: 978-92-4-1563697, pp: 15-25.
• T. Hanscheid, “Current strategies to avoid misdiagnosis of malaria”, Clin.Microbiol. Infect. 9 (2003) 497–504.
• WHO, Basic Malaria Microscopy. Part I. Learner’s Guide, World Health Organization, 1991, pp.6-14.
• M.M. Kettelhut, P.L. Chiodini, H. Edwards, A. Moody, “External quality assessment schemes raise standards: evidence from the UKNEQAS parasitology subschemes” , J. Clin. Pathol. 56 (2003) 927–932.
• I. Bates, V. Bekoe, A. Asamoa-Adu, “Improving the accuracy of malaria-related laboratory tests in Ghana”, Malar. J. 3 (2004) 38.
• F.B. Tek, A.G. Dempster, I. Kale, “Malaria parasite detection in peripheral blood images”, In: Proc. Med. Imaging Understand. Anal. Conf., Manchester, UK, 2006.
• F.B. Tek, A.G. Dempster, I. Kale, “Malaria parasite detection in peripheral blood images”, In: Proc. Br. Mach. Vis. Conf., Edinburgh, UK, 2006.
• S. Halim, T. Bretschneider, Y. Li, P. Preiser, C. Kuss,” Estimating malaria parasitaemia from blood smear images”, In: Proc. IEEE Int. Conf. Control Autom. Robot Vis., Singapore, 2006.
• S.W.S. Sio, W. Sun, S. Kumar, W.Z. Bin, S.S. Tan, S.H. Ong, H. Kikuchi, Y. Oshima, K.S.W. Tan, “Malariacount: an image analysis-based program for the accurate determination of parasitemia”, J. Microbiol. Methods 68 (2007) 11–18.
• N.E. Ross, C.J. Pritchard, D.M. Rubin, A.G. Duse, “Automated image processing method for the diagnosis and classification of malaria on thin blood smears”, Med. Biol. Eng. Comput. 44 (2006) 427–436.
• F.B. Tek, A.G. Dempster, I. Kale,” Computer vision for microscopy diagnosis of malaria”, Malar. J. 8 (2009), 153.
• Maombi Edison,J.B.Jeeva,Megha Singh,”Digital analysis of changes by plasmodium vivax malaria in erythrocytes”,Indian Journal of experimental Biology,Vol.49,January 2011,PP.11-15.
• Rafael C.Gonzalez,Richard E.Woods,”Digital Image processing”,(2nd edition) by prentice Hall,2002.
• You-bing Zgang;l KuiZhou;”study on automotive style recognition with the image erosion technology”,international conference on consumer electronics,communications and networks,2011,16-18,April,pp.4438-4441.
• DPDx-Malaria Image Library http://www.dpd.cdc.gov/dpdx/html/imagelibrary/malaria_il.htm
• Google images for malaria microscopic images http://www.google.co.in/search?q=google+images+for+malaria+ mic oscopic+images&hl=en&pwst=1&biw=1366&bih=667&prmd=ivn & tbm=isch&tbo=u&source=univ&sa=X&ei=bmtTTs77Os yrAeh 1JWzDg&ved=0CEQQsAQ
• S.S.Mohamed, A.M.Youssef, E.F.E.L.Sadaany and M.M.A.Salama ,” LLE based TRVS image features dimensionality reduction for prostate cancer diagnosis”, GVIP special issue on cancer diagnosis,2007, the international congress for global science and technology(ICGST).
• T.Jeinek ,M.P.Grobusch, S.Schwenke, S.Steidl,F.Von , sonnenburg,H.D. Nothdurft,E.Klein and T.Loscher,” Sensitivity and specificity of dipstick test for rapid diagnosis of malaria in nonimmune travellers”,Journal of clinical microbiology,Vol.37(3), PP.721-723,March,1999.
• F. Boray Tek, Andrew G. Dempster and Izzet Kale,“Malaria Parasite Detection in Peripheral Blood Images”,proceedings of the British Machine vision conference(BMVC 2006),UK,PP.347-356.
• Vishnu V.Makkapati,Raghuveer M.Rao,”Segmentation of malaria parasites in peripheral blood smear images”, International conference on acoustics, speech and signal processing(ICASSP),April,2009,pp.1361-1364.
• Gatti S,Bemuzzi AM,Bisoffi Z,Raglio A,Gulletta M, Scaglia M, “Multicentre study, in patients with imported malaria, on the sensitivity and specificity of a dipstick test (ICT Malaria P.f./P.v.) compared with expert microscopy”, Ann.Trop.Med.Parasitol,2002 Jan;96(1):15-8,
• Halim S,Bretschneider T.R,Yikun Li,Preiser P.R.,”Estimating malaria parasitaemia from blood smear images”, proceedings of the 9th international conference on control,automation,robotics and vision, PP.1-6,Dec.2006.
• C. Di Ruberto, A. Dempster, S. Khan, and B. Jarra, “Automatic thresholding of infected blood images using granulometry and regional extrema”, 441-444, 2000 IEEE.
• Tomasz Markiewicz, Stanislaw Osowski, “Automatic Recognition of the Blood Cells of Myelogenous Leukemia Using SVM”, Proceedings of International Joint Conference on Neural Networks, Canada,2496-2501, Aug. 2005.
• Nicola Ritter, James Cooper, “Segmentation and Border Identification of Cells in Images of Peripheral Blood Smear Slides”, The Thirtieth Australasian Computer Science Conference (ACSC2007), Australia. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 62, 2007.
• Gloria Diaz, Fabio A. Gonzalez, Eduardo Romero, “A Semi automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images”, Journal of Biomedical Informatics 42 (2009) 296–307.

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