TY - JOUR
T1 - Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy
AU - Poostchi, Mahdieh
AU - Ersoy, Ilker
AU - McMenamin, Katie
AU - Gordon, Emile
AU - Palaniappan, Nila
AU - Pierce, Susan
AU - Maud, Richard J.
AU - Bansal, Abhisheka
AU - Srinivasan, Prakash
AU - Miller, Louis
AU - Palaniappan, Kannappan
AU - Thoma, George
AU - Jaeger, Stefan
N1 - Publisher Copyright:
© 2018 SPIE.
PY - 2018/10/1
Y1 - 2018/10/1
N2 - 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.
AB - 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.
KW - Automated malaria diagnosis
KW - cell segmentation and classification
KW - computational microscopy imaging
KW - red blood cell infection
KW - thin blood smears
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UR - http://www.scopus.com/inward/citedby.url?scp=85058824984&partnerID=8YFLogxK
U2 - 10.1117/1.JMI.5.4.044506
DO - 10.1117/1.JMI.5.4.044506
M3 - Article
C2 - 30840746
AN - SCOPUS:85058824984
SN - 2329-4302
VL - 5
JO - Journal of Medical Imaging
JF - Journal of Medical Imaging
IS - 4
M1 - 044506
ER -