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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.
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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.
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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.
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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.
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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