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12 December 2018 Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy
Mahdieh Poostchi, Ilker Ersoy, Katie McMenamin, Emile Gordon, Nila Palaniappan, Susan Pierce, Richard J. Maude, Abhisheka Bansal, Prakash Srinivasan, Louis Miller, Kannappan Palaniappan, George Thoma, Stefan Jaeger
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Funded by: US National Institute of Health, Wellcome Trust of Great Britain
Journal of Medical Imaging, Vol. 5, Issue 4, 044506 (December 2018). https://doi.org/10.1117/1.JMI.5.4.044506
Abstract
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.
CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
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Mahdieh Poostchi, Ilker Ersoy, Katie McMenamin, Emile Gordon, Nila Palaniappan, Susan Pierce, Richard J. Maude, Abhisheka Bansal, Prakash Srinivasan, Louis Miller, Kannappan Palaniappan, George Thoma, and Stefan Jaeger "Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy," Journal of Medical Imaging 5(4), 044506 (12 December 2018). https://doi.org/10.1117/1.JMI.5.4.044506
Received: 16 August 2018; Accepted: 23 October 2018; Published: 12 December 2018
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Cited by 32 scholarly publications.
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KEYWORDS
Image segmentation
Blood
Flow cytometry
RGB color model
Microscopy
Detection and tracking algorithms
Image processing
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