Automated status identification of microscopic images obtained from malaria thin blood smears using bayes decision: A study case in plasmodium falciparum

Diagnosing malaria, as the first step to control the spread of the infectious disease, can be significantly optimized with a Computer Aided Diagnosis system. This study is proposed to develop a novel image processing algorithm to realiably detect the presence of malaria parasites from Plasmodium falciparum species in this smears of Giemsa stained peripheral blood sample. The proposed system was built using malaria samples that were specifically prepared by Eijkman Institute for Molecular Biology. Digital microphotographs were acquired using a digital camera connected to a light microscope. Global thresholding and connected component extraction were implemented to identify blood cell components. Two stage classification using separate set of features was built based on Bayes Decision Theory. Infected erythrocytes were identified with sensitivity of 92.59%, specificity of 99.65%, and PPV of 67.56%. The system provided an F₁ measure of 0.78.