Send to

Choose Destination
J Med Imaging (Bellingham). 2018 Oct;5(4):044506. doi: 10.1117/1.JMI.5.4.044506. Epub 2018 Dec 12.

Malaria parasite detection and cell counting for human and mouse using thin blood smear microscopy.

Author information

Lister Hill National Center for Biomedical Communications, National Library of Medicine, Bethesda, Maryland, United States.
University of Missouri-Columbia, Informatics Institute, Missouri, United States.
University of Colorado Boulder, Aerospace Engineering Sciences Department, Boulder, Colorado, United States.
National Institute of Allergy and Infectious Diseases, Division of Intramural Research, Rockville, Maryland, United States.
University of Missouri-Kansas City, School of Medicine, Kansas City, Missouri, United States.
University of Oxford, Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, Oxford, United Kingdom.
Mahidol University, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Bangkok, Thailand.
Harvard University, Harvard TH Chan School of Public Health, Boston, Massachusetts, United States.
Jawaharlal Nehru University, School of Life Sciences, New Delhi, India.
National Institute of Allergy and Infectious Diseases, Laboratory of Malaria and Vector Research, Rockville, Maryland, United States.
Johns Hopkins Bloomberg School of Public Health, Molecular Microbiology and Immunology, Baltimore, Maryland, United States.
University of Missouri-Columbia, Department of Electrical Engineering and Computer Science, Columbia, Missouri, United States.


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.


Automated malaria diagnosis; cell segmentation and classification; computational microscopy imaging; red blood cell infection; thin blood smears

[Available on 2019-12-12]

Supplemental Content

Full text links

Icon for Society of Photo-Optical Instrumentation Engineers Icon for PubMed Central
Loading ...
Support Center