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Content Based Image Retrieval Approaches for Detection of Malarial in Blood Images
Mohammad Imroze Khan, Bikesh Kumar Singh, Bibhudendra Acharya, Jigyasa Soni
Pages - 97 - 110     |    Revised - 01-05-2011     |    Published - 31-05-2011
Volume - 5   Issue - 2    |    Publication Date - May / June 2011  Table of Contents
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KEYWORDS
Falciparum, Vivax, Malariae, Giemsa
ABSTRACT
Malaria is a serious global health problem, and rapid, accurate diagnosis is required to control the disease. An image processing algorithm to automate the diagnosis of malaria in blood images is proposed in this paper. The image classification system is designed to positively identify malaria parasites present in thin blood smears, and differentiate the species of malaria. Images are acquired using a charge-coupled device camera connected to a light microscope. Morphological and novel threshold selection techniques are used to identify erythrocytes (red blood cells) and possible parasites present on microscopic slides. Image features based on colour, texture and the geometry of the cells and parasites are generated, as well as features that make use of a priori knowledge of the classification problem and mimic features used by human technicians. A two-stage tree classifier using backpropogation feedforward neural networks distinguishes between true and false positives, and then diagnoses the species (Plasmodium falciparum, P. vivax, P. ovale or P. malariae) of the infection. Malaria samples obtained from the various biomedical research facilities are used for training and testing of the system. Infected erythrocytes are positively identified with two measurable parameters namely sensitivity and a positive predictive value (PPV), which makes the method highly sensitive at diagnosing a complete sample, provided many views are analyzed.
CITED BY (14)  
1 Chiroma, H., Abdul-kareem, S., Ibrahim, U., Ahmad, I. G., Garba, A., Abubakar, A., ... & Herawan, T. (2015).malaria severity classification through jordan-elman neural network based on features extracted from thick blood SMEAR.Neural Network World, 25(5), 565.
2 Razzak, M. I. (2015). Automatic Detection and Classification of Malarial Parasite. International Journal of Biometrics and Bioinformatics (IJBB), 9(1), 1.
3 Shirazi, S. H., Umar, A. I., Haq, N. U., Naz, S., & Razzak, M. I. (2015). Accurate Microscopic Red Blood Cell Image Enhancement and Segmentation. In Bioinformatics and Biomedical Engineering (pp. 183-192). Springer International Publishing.
4 Yeon, J., Kim, J. D., Park, C. Y., Kim, Y. S., & Song, H. J. (2015). Comparison of Grayscale Conversion Methods for Malaria Classification. International Journal of Bio-Science & Bio-Technology, 7(1).
5 Razzak, I. (2015). Malarial Parasite Classification using Recurrent Neural Network. International Journal of Image Processing (IJIP), 9(2), 69.
6 DAS, D., Mukherjee, R., & Chakraborty, C. (2015). Computational microscopic imaging for malaria parasite detection: a systematic review. Journal of microscopy.
7 Somasekar, J., & Reddy, B. E. (2015). Segmentation of erythrocytes infected with malaria parasites for the diagnosis using microscopy imaging. Computers & Electrical Engineering.
8 Uc-Cetina, V., Brito-Loeza, C., & Ruiz-Piña, H. (2015). Chagas Parasite Detection in Blood Images Using AdaBoost. Computational and mathematical methods in medicine, 2015.
9 Kaur, J., & Kaur, K. (2014). Review of Content Based Retrieval of Malarial Positive Images from Clinical Database. Journal of Emerging Technologies in Web Intelligence, 6(4), 381-387.
10 Kaur, J., & Kaur, K. (2014).content based retrieval of malarial positive images.
11 Chiroma, H., Abdul-kareem, S., Ibrahim, U., Ahmad, I. G., Garba, A., Abubakar, A., & Herawan, T. (2014). Malaria severity classification through jordan–elman neural network based on features extracted from thick blood smear. Neural Network World.
12 Uc-Cetina, V., Brito-Loeza, C., & Ruiz-Piña, H. (2014). Chagas parasites detection through Gaussian discriminant analysis. Abstraction and Application Magazine, 8.
13 Sarkar, P., Chakraborty, C., & Ghosh, M. (2012, November). Content-based leukocyte image retrieval ensembling quaternion fourier transform and gabor-wavelet features. In Intelligent Systems Design and Applications (ISDA), 2012 12th International Conference on (pp. 345-350). IEEE.
14 Nasir, A. A., Mashor, M. Y., & Mohamed, Z. Colour image segmentation of malaria parasites in thin blood smears using CY colour model and K-Means clustering.
1 Google Scholar 
2 CiteSeerX 
3 refSeek 
4 iSEEK 
5 Libsearch 
6 Bielefeld Academic Search Engine (BASE) 
7 Scribd 
8 WorldCat 
9 SlideShare 
10 PdfSR 
Anthony Moody, Rapid Diagnostic Tests for Malaria Parasites, Clinical Microbiology Reviews,0893-8512/02/$04.00_0 DOI: 10.1128/CMR.15.1.66–78.2002, p. 66–78, Jan. 2002.
Baird J.K., Purnomo, Jones T.R. Diagnosis of malaria in the field by fluorescence microscopy of QBC ® capillary tubes. Transactions of the Royal Society of Tropical Medicine and Hygiene; 86: 3-5, 1992.
Bloland PB (2001) Drug resistance in malaria, WHO/CDS/CSR/DRS/ 2001.4. World Health Organization, Switzerland, 2001.
Brown A.E., Kain K.C., Pipithkul J., Webster H.K. “Demonstration by the polymerase chain reaction of mixed Plasmodium falciparum and P. vivax infections undetected by conventional microscopy”. Transactions of the Royal Society of Tropical Medicine and Hygiene; 86: 609-612, 1992.
Di Ruberto C, Dempster A, Khan S, Jarra B, “Analysis of infected blood cell images using morphological operators”. Image Vis Comput 20(2):133–146, 2002.
Di Ruberto, A. Dempster, S. Khan, and B. Jarra. “Automatic thresholding of infected blood images using granulometry and regional extrema”. In ICPR, pages 3445–3448, 2000.
F. Boray Tek, Andrew G. Dempster and Izzet Kale, “Malaria Parasite Detection in Peripheral Blood Images”, Applied DSP & VLSI Research Group, London, UK, Dec 2006.
F. Castelli, G.Carosi, Diagnosis of malaria, chapter 9, Institute of Infectious and Tropical Diseases, University of Brescia (Italy).
Foster S, Phillips M, Economics and its contribution to the fight against malaria. Ann Trop MedParasitol 92:391–398, 1998.
Gilles H.M. “The differential diagnosis of malaria. Malaria. Principles and practice of malariology (Wernsdorfer W.H., McGregor I eds)”, 769-779, 1998.
J.N. Kapur, P.K. Sahoo, and A.K.C. Wong, “A new method for gray-level picture thresholding using the entropy of the histogram”. Graphical Models and Image Processing, 29:273.285, 1985.
Jean-Philippe Thiran, Benoit Macq, “Morphological Feature Extraction for the Classification of Digital Images of Cancerous Tissues”. IEEE Transaction on Biomedical Engineering, Vol. 43, no. 10, October 1996.
Makler MT, Palmer CJ, Alger AL, “A review of practical techniques for the diagnosis of malaria”. Ann Trop Med Parasitol 92(4):419–433, 1998.
Mui JK, Fu K-S, “Automated classification of nucleated blood cells using a binary tree classifier”. IEEE Trans Pattern Anal Machine Intell 2(5):429–443, 1980
N. Otsu, “A threshold selection method from gray-level histograms”. IEEE Transactions on Systems, Man and Cybernetics, 9(1):62.66, 1979.
Rafeal C. Gonzalez, Richard E. Woods, Digital Image Processing, 2nd Edition, Prentice Hall, 2006.
S. M. Smith, J. M. Bardy, “SUSAN - A New Approach to Low Level Image Processing”, International Journal of Computer Vision, Volume 23, and Issue 1 Pages: 45 – 78, may 1997.
Selena W.S. Sio, Malaria Count, “An image analysis-based program for the accurate determination of parasitaemia, Laboratory of Molecular and Cellular Parasitology”, Department of Microbiology, Yong Loo Lin School of Medicine, National University of Singapore. May 2006.
Silvia Halim, Timo R. Bretschneider, Yikun Li, “Estimating Malaria Parasitaemia from Blood Smear Images”. 1-4244-0342-1/06/$20.00 ©IEEE, ICARCV 2006.
T.W. Ridler and S. Calvard, “Picture thresholding using an iterative selection method”. IEEE Transactions on Systems, Man and Cybernetics, SMC-8:630.632, 1978.
World Health Organization. What is malaria? Facts sheet no 94. http://www.who.int/mediacentre/factsheets/fs094/en/.
Mr. Mohammad Imroze Khan
N.I.T., Raipur - India
imroze786@gmail.com
Mr. Bikesh Kumar Singh
- India
Mr. Bibhudendra Acharya
- India
Mr. Jigyasa Soni
- India


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