Home   >   CSC-OpenAccess Library   >    Manuscript Information
Automated Protocol for Counting Malaria Parasites (P. falciparum) from Digital Microscopic Image Based on L*a*b* Colour Model and K-Means Clustering
J. Opoku-Ansah, B. Anderson, J. M. Eghan, J. N. Boampong, P. Osei-Wusu Adueming, C. L. Y. Amuah, A. G. Akyea
Pages - 149 - 158     |    Revised - 01-10-2013     |    Published - 01-11-2013
Volume - 7   Issue - 4    |    Publication Date - November 2013  Table of Contents
MORE INFORMATION
KEYWORDS
Malaria Parasites, Image processing, Segmentation, K-means, cluster, L*a*b* colour model.
ABSTRACT
Basis for malaria parasites diagnosis in most hospitals and clinics, especially in developing countries, which is manually done, is strenuous and time-consuming. In this paper, we present an automated protocol for counting malaria parasites (P. falciparum) from digital microscopic red blood cells (RBCs) mages based on L*a*b* colour model and K-Means clustering algorithm using Matlab. This method is device-independent, perceptually uniform and approximates human vision. An image slide of size 300 x 300 x 3 pixels of RBCs with malaria parasites has been counted in less than 10 seconds using a computer with 64-bit Intel (R) Celeron (R) Central Processing Unit and processing speed of 2.20 GHz. The digital counts have a good correlation with the manual counts. This automated protocol has the potential of providing fast, accurate and objective detection information for proper clinical management of patients.
CITED BY (1)  
1 Opoku-Ansah, J., Eghan, M. J., Anderson, B., Boampong, J. N., & Buah-Bassuah, P. K. (2016). Laser-Induced Autofluorescence Technique for Plasmodium falciparum Parasite Density Estimation. Applied Physics Research, 8(2), 43.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 Scribd 
5 SlideShare 
6 PdfSR 
A. Hanbury, J. Serra. “Mathematical Morphology in the L*a*b* Colour Space” Technical report N-36/01/M, Centre of Mathematical Morphology, France, 2001.
C. Di Ruberto et al., “Analysis of infected blood cell images using morphological operators” Image and Vis. Comput. 20, pp. 133-146, 2002.
C. Di Ruberto et al., “Morphological Image Processing For Evaluating Malaria Disease” in Proc. Int Workshop on Visual Form, Capri, Italy, 2001.
C. Shiff. “Integrated approach for malaria control ” Clin. Microbiol. Rev. 15, pp. 278-293, 2002.
C.E. Contreras et al., “Stage specific activity of potential antimalarial compounds measured in vitro by flow cytometry in comparison to optical microscopy and hypoxanthine uptake,” presented at Mem. Inst. Oswaldo Cruz, Rio de Janeiro 99, Brazil, 2004.
Center for Disease Control and Prevention (CDC) (2010) Retrieved from http://phil.cdc.gov/phil/home.asp on November ?12 2010.
D. Mery, F. Pedreschi. “Segmentation of colour food images using a robust Algorithm” Journal of Food Engineering 66, pp. 353-360, 2005.
D. Payne “Use and limitations of light microscopy for diagnosing malaria at the primary health care level” Bulletin of the World Health Organization 66, pp. 621-626, 1988.
E. Korenromp et al., World Malaria Report 2005. In Tech rep. World Health Organization, Geneva, 2005.
F.B. Tek et al. “Computer vision for microscopy diagnosis of Malaria” Malaria Journal, 13 July 2009.
F.B. Tek et al., “Malaria Parasite Detection In Peripheral Blood Images” in Proc Med Image Underst. and Anal Conf, Manchester, UK , 2006.
F.B. Tek, et al., “Malaria Parasite Detection In Peripheral Blood Images,” in Proc Br Mach Vis Conf, Edinburgh, UK, 2006.
G. Mittal et al., “An efficient video enhancement method using L*a*b* analysis” IEEE Computer Society 79, pp. 66-70, 2006.
H. Noedl, C. Wongsrichanalai and W.H. Wernsdorfer. “Malaria drug sensitivity testing: new assays, new perspectives” Trends Parasitol 19, pp. 175-181, 2003.
I. Bates, et al., “Improving the accuracy of malaria-related laboratory tests in Ghana” Malar J., 01 Nov. 2004.
K. León et al., “Color measurement in L*a*b* units from RGB digital images” Food Research International 39, pp. 1084-1091, 2006.
K. Mitiku et al., “The reliability of blood film examination for malaria at the peripheral health unit” Ethiop J. Health Dev 17, pp. 197-204, 2003.
L. Kaufman and P.J. Rousseeuw. Finding groups in data: An introduction to cluster analysis, John Wiley & Sons Inc., Hoboken New Jersey, 2005, pp. 155-160.
M.D. Pammenter. “Techniques for the diagnosis of malari.” S. Afr. Med J. 74, pp. 55-57, 1988.
M.T. Makler, et al., “A review of practical techniques for the diagnosis of malaria ” Annals of Tropical Medicine & Parasitology 92, pp. 419-433, 1998.
Mathworks Inc., R2010a MATLAB 7.10.0, 2010.
N. Otsu. “A Threshold Selection Method from Gray-Level Histograms” IEEE Transactions on Systems, Man and Cybernetics, 9 (1), pp. 62-66, 1979.
N.E. Ross et al., “Automated image processing method for the diagnosis and classification of malaria on thin blood smears” Medical and Biological Engineering and Computing 44, pp. 427-436, 2006.
P.B. Bloland. “Drug resistance in malaria,” presented at WHO/CDS/CSR/DRS/ 2001.4, World Health Organization, Switzerland, 2001.
R.E. Coleman et al., “Comparison of field and expert laboratory microscopy for active surveillance for asymptomatic Plasmodium falciparum and Plasmodium vivax in Western Thailand” Am J Trop Med Hyg. 67, pp. 141-144, 2002.
R.S. Phillips. “Current status of malaria and potential for control” Clin. Microbiol. Rev.14, pp. 208-226, 2001.
R.W.G. Hunt. “Measurement of Colour Appearance” J. Opt. Soc. Am. 55, pp. 1540-1551, 1965.
S. Halim, et al. “Estimating Malaria Parasitaemia From Blood Smear Images,” in Proc. IEEE Int. Conf. Control Autom. Robot and Vis, Singapore, 2006.
S.C.J. Oaks et al., “Malaria: obstacles and opportunities, A report of the committee for the study on malaria prevention and control: Status review and alternative strategies,” National Academy Press, Washington, DC, 1991.
S.S. Savkare, S.P. Narote. “Automatic Detection of Malaria Parasites for Estimating Parasitemia” International Journal of Computer Science and Security (IJCSS) 5, pp. 310-315, 2011.
S.W.S. Sio et al., “Malaria Count: An image analysis-based program for the accurate determination of parasitemia” Journal of Microbiological Methods 68, Science Direct, pp. 11-18, 2007.
W.H.O. Basic malaria microscopy Part I Learner's Guide, World Health Organization, 1991, pp. 7-10.
W.L. Martinez and A.R. Martinez. Exploratory Data Analysis with MATLAB®, Computer Science and Data Analysis Series, Chapman & Hall/CRC, 2005, pp. 135-139.
Mr. J. Opoku-Ansah
School of Physical Sciences Department of Physics, Laser and Fibre Optics Centre (LAFOC), University of Cape Coast, Cape Coast - Ghana
Mr. B. Anderson
School of Physical Sciences Department of Physics, Laser and Fibre Optics Centre (LAFOC), University of Cape Coast, Cape Coast - Ghana
Dr. J. M. Eghan
School of Physical Sciences Department of Physics, Laser and Fibre Optics Centre (LAFOC), University of Cape Coast, Cape Coast - Ghana
meghan@ucc.edu.gh
Mr. J. N. Boampong
School of Biological Sciences Department of Biomedical and Forensic Sciences, University of Cape Coast, Cape Coast - Ghana
Mr. P. Osei-Wusu Adueming
School of Physical Sciences Department of Physics, Laser and Fibre Optics Centre (LAFOC), University of Cape Coast, Cape Coast - Ghana
Mr. C. L. Y. Amuah
School of Physical Sciences Department of Physics, Laser and Fibre Optics Centre (LAFOC), University of Cape Coast, Cape Coast - Ghana
Miss A. G. Akyea
School of Physical Sciences Department of Physics, Laser and Fibre Optics Centre (LAFOC), University of Cape Coast, Cape Coast - Ghana