Mahdieh PoostchiIlker ErsoyKatie McMenaminEmile GordonNila PalaniappanSusan PierceRichard J. MaudAbhisheka BansalPrakash SrinivasanLouis MillerKannappan PalaniappanGeorge ThomaStefan JaegerHarvard School of Public HealthJawaharlal Nehru UniversityNational Institute of Allergy and Infectious DiseasesMahidol UniversityNuffield Department of Clinical MedicineUniversity of Missouri-Kansas CityJohns Hopkins Bloomberg School of Public HealthUniversity of Missouri-ColumbiaNational Library of MedicineUniversity of Colorado at Boulder2019-08-232019-08-232018-10-01Journal of Medical Imaging. Vol.5, No.4 (2018)23294310232943022-s2.0-85058824984https://repository.li.mahidol.ac.th/handle/20.500.14594/46309© 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.Mahidol UniversityMedicineArticleSCOPUS10.1117/1.JMI.5.4.044506