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.
Highlights
Malaria is caused by parasites transmitted via bites of female Anopheles mosquitoes
We evaluate the performance of coupled edge profile active contours (C-EPAC) on stained blood slides for malaria diagnosis that is new since RGB blood slide images have an entirely different characteristic than blood flow videos and accurate segmentation is essential to a successful cell classification
We evaluated the performance on both support vector machine (SVM) and artificial neural network (ANN) to show that the best feature set outperforms other features independent from
Summary
Malaria is caused by parasites transmitted via bites of female Anopheles mosquitoes. Parasite-infected red blood cells (RBCs) lead to symptoms, such as fever, malaise, seizures, and coma, in severe cases. We present an end-to-end automated detection system for identifying and quantifying malaria parasites (P. falciparum) in thin blood smears of both human and mouse. We evaluate the performance of C-EPAC on stained blood slides for malaria diagnosis that is new since RGB blood slide images have an entirely different characteristic than blood flow videos and accurate segmentation is essential to a successful cell classification. We use a combination of color and texture features to characterize segmented RBCs. We develop an offline feature evaluation framework using manually annotated cells to select the most discriminative features, reduce feature dimensionality, and improve classification performance [Fig. 1(c)]. We use a combination of low-level complementary features to encode both color and texture information of RBCs. Features are selected through an offline evaluation framework to optimize the classification performance using manually annotated cells.
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