Automatic Detection of Malaria Parasite based on Microscopic Image Analysis

Automatic Detection of Malaria Parasite based on Microscopic Image Analysis Abebe Bekele Addis Ababa University, 2017 Malaria is a serious global health problem and its diagnosis is usually done manually by compound light microscopy which is time consuming, tiresome and subjective. To support this manual method, in this master thesis, we designed and developed a system which is able to automatically detect plasmodium parasites from images of blood smears acquired by ourselves using a digital light microscope. In this method, blood smears taken from patients who were infected with plasmodium parasites were prepared. Digital images were then acquired by the light microscope and saved in the computer. Red blood cells (RBCs) are first segmented by marker control watershed algorithm, where the foreground markers are obtained from circular Hough transform and background markers from distance transform. The plasmodium infected RBCs are then detected in the Hue-Saturation-Intensity (HSI) color space. Thresholding on hue component of HSI color space is used to detect the chromatin dots of the parasite. Plasmodium falciparum and plasmodium vivax, the two dominant plasmodium species which cause the vast deaths in Ethiopia, are differentiated based on the size of infected RBCs. The performance of the proposed system for RBC segmentation, parasite detection and species differentiations was analyzed by comparing with the gold standard manual method for the total of 91 images of thin blood smears. The result shows that 97% of the RBC counts are similar to the gold standard with 97.5% sensitivity and 84.4% positive predictive value for plasmodium parasite detection at the cellular level. The species differentiations were done for each image with the accuracy level of 91.46%.The result showed the potential of the method for supporting the mass screening of malaria parasite. Keywords: Digital Microscope, Plasmodium, Thin Blood Smears, Watershed Algorithm, Circular Hough Transform, Distance Transform, Hue-Saturation-Intensity (HSI), Thresholding