Publication: Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy
Issued Date
2018-10-01
Resource Type
ISSN
23294310
23294302
23294302
Other identifier(s)
2-s2.0-85058824984
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Mahidol University
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SCOPUS
Bibliographic Citation
Journal of Medical Imaging. Vol.5, No.4 (2018)
Suggested Citation
Mahdieh Poostchi, Ilker Ersoy, Katie McMenamin, Emile Gordon, Nila Palaniappan, Susan Pierce, Richard J. Maud, Abhisheka Bansal, Prakash Srinivasan, Louis Miller, Kannappan Palaniappan, George Thoma, Stefan Jaeger Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy. Journal of Medical Imaging. Vol.5, No.4 (2018). doi:10.1117/1.JMI.5.4.044506 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/46309
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Title
Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy
Other Contributor(s)
Harvard School of Public Health
Jawaharlal Nehru University
National Institute of Allergy and Infectious Diseases
Mahidol University
Nuffield Department of Clinical Medicine
University of Missouri-Kansas City
Johns Hopkins Bloomberg School of Public Health
University of Missouri-Columbia
National Library of Medicine
University of Colorado at Boulder
Jawaharlal Nehru University
National Institute of Allergy and Infectious Diseases
Mahidol University
Nuffield Department of Clinical Medicine
University of Missouri-Kansas City
Johns Hopkins Bloomberg School of Public Health
University of Missouri-Columbia
National Library of Medicine
University of Colorado at Boulder
Abstract
© 2018 SPIE. Despite the remarkable progress that has been made to reduce global malaria mortality by 29% in the past 5 years, malaria is still a serious global health problem. Inadequate diagnostics is one of the major obstacles in fighting the disease. An automated system for malaria diagnosis can help to make malaria screening faster and more reliable. We present an automated system to detect and segment red blood cells (RBCs) and identify infected cells in Wright-Giemsa stained thin blood smears. Specifically, using image analysis and machine learning techniques, we process digital images of thin blood smears to determine the parasitemia in each smear. We use a cell extraction method to segment RBCs, in particular overlapping cells. We show that a combination of RGB color and texture features outperforms other features. We evaluate our method on microscopic blood smear images from human and mouse and show that it outperforms other techniques. For human cells, we measure an absolute error of 1.18% between the true and the automatic parasite counts. For mouse cells, our automatic counts correlate well with expert and flow cytometry counts. This makes our system the first one to work for both human and mouse.
