Machine aided malaria parasitemia detection in Giemsa-stained thin blood smears

Abstract

Malaria parasitemia is the quantitative measurement of the parasites in the blood to grade the degree of infection. Light microscopy is the most well-known method used to examine the blood for parasitemia quantification. The visual quantification of malaria parasitemia is laborious, time-consuming and subjective. Although automating the process is a good solution, the available techniques are unable to evaluate the same cases such as anemia and hemoglobinopathies due to deviation from normal RBCs’ morphology. The main aim of this research is to examine the microscopic images of stained thin blood smears using a variety of computer vision techniques, grading malaria parasitemia on independent factors (RBC’s morphology). The proposed methodology is based on inductive approach, color segmentation of malaria parasites through adaptive algorithm of Gaussian mixture model (GMM). The quantification accuracy of RBCs is improved, splitting the occlusions of RBCs with distance transform and local maxima. Further, the classification of infected and non-infected RBCs has been made to properly grade parasitemia. The training and evaluation have been carried out on image dataset with respect to ground truth data, determining the degree of infection with the sensitivity of 98 % and specificity of 97 %. The accuracy and efficiency of the proposed scheme in the context of being automatic were proved experimentally, surpassing other state-of-the-art schemes. In addition, this research addressed the process with independent factors (RBCs’ morphology). Eventually, this can be considered as low-cost solutions for malaria parasitemia quantification in massive examinations.