Analysis of Colour Constancy Algorithms for Improving Segmentation of Malaria Images
Keywords:Colour Constancy, Colour Standardization, Image Segmentation, Malaria,
Malaria is a very serious disease that caused by the transmitted of parasites through the bites of infected Anopheles mosquito. Malaria death cases can be reduced and prevented through early diagnosis and prompt treatment. Currently, microscopy-based diagnosis remains the most widely used approach for malaria diagnosis. The appearance of the infected red blood cells (RBCs) and their morphological features are very important for recognising the presence of malaria parasites. However, it is difficult to identify the presence of malaria parasites as well as observing its morphological characteristics due to the non-standard preparation of the blood slides; producing colour varieties in different slides. Thus, this study aims to apply colour constancy algorithms for standardisation of blood images in order to enhance segmentation of malaria parasites. In this paper, four different colour constancy algorithms namely Gray-World, white patch, modified white patch and progressive algorithms have been analysed to identify colour constancy algorithm that can give the significant segmentation performance. The experimental results show that segmentation on Gray-World images has successfully segmented 100 malaria images with average segmentation accuracy, sensitivity and specificity of 99.60%, 91.26% and 99.85%, respectively.
World Health Statistics 2017. World Health Organization, 2017.
World Malaria Report 2015. World Health Organization, 2016.
Basic Malaria Microscopy, Part I. Learner’s Guide. World Health Organization, 2010.
C.C.A. Azikiwe, C.C. Ifezulike, I.M. Siminialayi, L.U. Amazu, J.C. Enye,and O.E. Nwakwunite, “A comparative laboratory diagnosis of malaria: microscopy versus rapid diagnostic test kits,” Asian Pacific Journal of Tropical Biomedicine, vol. 2, pp. 307–310, Apr. 2012.
V.V. Panchbhai, L.B. Damahe, A.V. Nagpure, and P.N. Chopkar, “RBCs and parasites segmentation from thin smear blood cell images,” International Journal of Image, Graphics and Signal Processing, vol. 4, pp. 54, Sep. 2012.
D. Das, M. Ghosh, C. Chakraborty, A.K. Maiti, and M. Pal, “Probabilistic prediction of malaria using morphological and textural information,” in 2011 International Conference on Image Information Processing, pp. 1–6.
S. S. Savkare and S.P. Narote, “Automatic detection of malaria parasites for estimating parasitaemia,” International Journal of Computer Science and Security, vol. 5, pp. 310, Jul. 2011.
J.E. Arco, J.M. Górriz, J. Ramírez, I. Álvarez, and C.G. Puntonet, “Digital image analysis for automatic enumeration of malaria parasites using morphological operations,” Expert Systems with Applications, vol. 42, pp. 3041–3047, Apr. 2015.
S. Mandal, A. Kumar, J. Chatterjee, M. Manjunatha, and A.K. Ray, “Segmentation of blood smear images using normalized cuts for detection of malarial parasites,” in 2010 Annual IEEE India Conference, pp. 1–4.
J. Somasekar and B.E. Reddy, “Segmentation of erythrocytes infected with malaria parasites for the diagnosis using microscopy imaging,” Computers & Electrical Engineering, vol. 45, pp. 336–351, Jul. 2015.
A.S. Abdul-Nasir, M.Y. Mashor, N.H.A. Halim, and Z. Mohamed, “The cascaded moving k-means and fuzzy c-means clustering algorithms for unsupervised segmentation of malaria images,” in AIP Conference Proceedings 2015.
F.B. Tek, Computerised Diagnosis of Malaria, PhD thesis, University of Westminster, 2007.
A.S. Abdul-Nasir, M.Y. Mashor, and Z. Mohamed, “Modified global and modified linear contrast stretching algorithms: New colour contrast enhancement techniques for microscopic analysis of malaria slide image,” Computational and mathematical methods in medicine, Oct. 2012.
G. Buchsbaum, “A spatial processor model for object colour perception,” Journal of the Franklin Institute, vol. 310, pp. 1–26, Jul. 1980.
E.H. Land, “The Retinex Theory of Color Vision,” Scientific Am., vol. 237, pp. 108–128, Dec. 1977.
M. Chambah, B. Besserer, and P. Courtellemont, “Recent Progress in Automatic Digital. Restoration of Color Motion Pictures,” in SPIE Electronic Imaging 2002, pp.98–109.
C. Di Ruberto, A. Dempster, S. Khan, and B. Jarra, “Analysis of infected blood cell images using morphological operators,” Image and vision computing, vol. 20, pp. 133–146, Feb. 2002.
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