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| Automatic Detection of Malaria Parasites for Estimating Parasitemia
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Full
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Source |
International Journal of Computer Science and Security (IJCSS) |
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Table of Contents |
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Complete Issue PDF(2.93MB) |
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Volume: 5 Issue: 3 |
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Pages: 298-393 |
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Publication
Date: July / August 2011 |
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ISSN
(Online): 1985-1553 |
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Pages |
310 - 315 |
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Author(s) |
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Published
Date |
05-08-2011 |
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Publisher |
CSC
Journals, Kuala Lumpur,
Malaysia |
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ADDITIONAL
INFORMATION |
| Keywords Abstract References Cited by Related Articles Collaborative
Colleague |
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KEYWORDS: OTSU Thresholding, Watershed Transform, Feature Extraction, SVM Classifier |
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| Malaria parasitemia is a measurement of the amount of Malaria parasites in the patient's blood and an indicator for the degree of infection. In this paper an automatic technique is proposed for Malaria parasites detection from blood images by extracting red blood cells (RBCs) from blood image and classifying as normal or parasite infected. Manual counting of parasitemia is tedious and time consuming and need experts. Proposed automatic approach is used Otsu thresholding on gray image and green channel of the blood image for cell segmentation, watershed transform is used for separation of touching cells, color and statistical features are extracted from segmented cells and SVM binary classifier is used for classification of normal and parasite infected cells. |
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Shiff, C., 2002. Integrated approach for malaria control. Clin. Microbiol. Rev.15, 278–293.
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World Health Organization What is malaria? Factssheetno94. http://www.who.int/mediacentrefactsheetsfs094/en/./factshee. J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp 68–73.
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M. Tam Le, T. Bretschneider,“A novel semi-automatic image processing approach to determine Plasmodium falciparum parasitemia in Giemsa-stained thin blood smears”, Research article, BMC Cell Biology, 28 March 2008.
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J. Angulo, G. Flandrin, “Automated detection of working area of peripheral blood smears using mathematical morphology”, U. S. National Library of Medicine, Analytical Cellular Pathology 25(1), pp 39-47, 2003.
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C.D. Ruberto, A.G. Dempster, S. Khan, and B. Jarra, "Automatic Thresholding of Infected Blood Images Using Granulometry and Regional Extrema", in Proceedings of International Conference on Pattern Recognition, pp 3445-3448, 2000.
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S. Halim et al., “Estimating Malaria Parasitaemia from Blood Smear Images”, in Proceedings of IEEE international conference on control, automation, robotics and vision, pp 1-6, 2006.
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S. Osowski et al., “Application of Support Vector Machine and Genetic Algorithm for Improved Blood Cell Recognition”, in proceedings of IEEE transaction on Instrumentation and Measurement, Vol. 58, No. 7, pp 2159-2168, July 2009.
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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.
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D. Anoraganingrum et al.’ “Cell Segmentation with Median Filter and Mathematical Morphology Operation”, in proceedings of on Image Analysis and Processing, Italy, pp 1043-1046, 1999.
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N. Otsu, “A threshold selection method from gray-level histograms”, in proceedings of IEEE Transactions on Systems, Man and Cybernetics, 9(1), pp 62-66, 1979.
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K. Kim et al., “Automatic Cell Classification in Human's Peripheral Blood Images Based on Morphological Image Processing”, Lecture Notes in Computer Science, vol. 2256. pp 225-236, 2001.
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T. Markiewicz, S. Osowski, “Data mining techniques for feature selection in blood cell recognition”, European Symposium on Artificial Neural Networks, Bruges (Belgium), 26-28 April, pp 407-412, 2006
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N. Ritter, J. Cooper, “Segmentation and Border Identification of Cells in Images of Peripheral Blood Smear Slides”, in Proceedings of Thirtieth Australasian Computer Science Conference (ACSC2007), CRPIT, 62, 161-169, 2007.
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G. Diaz et al., “A semi-automatic method for quantification and classification of erythrocytes infected with malaria parasites in microscopic images”, Journal of Biomedical Informatics 42, Science Direct, pp 296–307, 2009.
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Prof. S. K. Bandyopadhyay and S. Roy, “Detection of Sharp Contour of the element of the WBC and Segmentation of two leading elements like Nucleus and Cytoplasm”, International Journal of Engineering Research and Applications (IJERA), 2(1), pp.545-551, Jan-Feb 2012.
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| S. S. Savkare : Colleagues
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| S. P. Narote : Colleagues
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