1-UNICEF. World Malaria Report:Technical Report: WMR and UNICEF. 2012.Unicef Health. Available from: http://www.unicef.org/health/index_malaria.html. Accessed Jul 15, 2013.
2-Gallup J, Sachs J. The economic burden of malaria. Journal of Tropical Medicine 2001; 64 (Suppl 1-2): 85-96.
3-Coatney G, Collins W, Warren M, Contacos P. The Primate Malarias . Washington: National Academy Press; 1971.
4-Mui JK, Fu KS. Automated classification of nucleated blood cells using a binary tree classifier. IEEE Trans Pattern Analysis and Machine Intelligence 1980; 2(5): 429-43.
5-Otsu N. A threshold selection method from gray-level histograms. IEEE Trans Systems, Man & Cybernetics 1979; 9 (1): 62–6.
6-Korn TM, Korn GA, editors. Mathematical Handbook for Scientists and Engineers.New York: Dover Publications; 2000 .
7-Rodenaker K, Bengtsson E. A feature set for cytometry on digitized microscopic images. Anal Cell Pathol 2003; 25(2): 1-36.
8- Boray TF. "Computerised Diagnosis of Malaria". PhD Thesis. London: University of Westminster, 2007.
9-Ruberto CD, Dempster A, Khan S, Jarra B. Analysis of infected blood cell images using morphological operators. Image Vision Comput 2002; 20:133–46.
10-Yang PF, Maragos P. Morphological systems for character image processing and recognition. In:Proceedings of IEEE Int Acoustics Conference; 1993 Sep 13-15; Minneapolis, USA. New York; 2002.
11-Rao KNRM, Dempster A. Area-granulometry: an improved estimator of size distribution of image objects. IEE Electron Lett 2001; 347(4): 950-51.
12-Theodoridis S, Koutroumbas K. Pattern recognition and neural networks. Machine Learning and Its Applications 2001; 12(2): 169-15.
13-Lotte F, Congedo M, Lécuyer A, Lamarche F, Arnaldi B. A review of classification algorithms for EEG-based brain–computer interfaces. Journal of neural engineering 2005; 4(2): 12-5.
14-Shashua A. the relationship between the support vector machine for classification and sparsified Fisher's linear discriminant. Neural Processing Letters 1999; 9(2): 129-39.
15-Tek FB, Dempster AG, Kale I. Parasite detection and identification for automated thin blood film malaria diagnosis. Computer Vision and Image Understanding 2010; 114: 21–32.
16-Ruberto CD, Dempster A, Khan S, Jarra B. Analysis of infected blood cell images using morphological operators. Image Vis. Comput 2002; 20)2): 133–46.
17-Ross NE, Pritchard CJ, Rubin DM, Dusé AG. Automated image processing method for the diagnosis and classification of malaria on thin blood smears. Med Biol Eng Comput 2006; 44: 427–36.
18- SWS S, Sun W, Kumar S, Bin WZ, Tan SS. MalariaCount: An image analysis-based program for the accuratedetermination of parasitemia. Journal of Microbiological Methods 2006; 2)2):13–6.
19-Diaz G, Gonzalez F, Romero E. Infected Cell Identification in thin Blood Images Based on Color Pixel Classification: Comparison and Analysis. J Biomed Inform 2007; 4756: 812-21.
20-Kumarasamy SK, Ong SH, Tan KSW. Robust contour reconstruction of red blood cells and parasites in the automated identification of the stages of malarial infection. Machine Vision and Applications springer 2011; 22(1): 461-9.
21-Das D, Ghosh M, Chakraborty C. Probabilistic Prediction of Malaria using Morphological and Textural Information. India, Bengal: International Conference on Image Information Processing (ICIIP); 2011.
)