Malaria Parasite Detection in Peripheral Blood Images

This paper investigates the possibility of computerised diagnosis of malaria and describes a method to detect malaria parasites (Plasmodium spp) in images acquired from Giemsa-stained peripheral blood samples under conventional light microscopes. Prior to processing, the images are transformed to match a reference image colour characteristics. The parasite detector utilises a Bayesian pixel classifier to mark stained pixels. The class conditional probability density functions of the stained and the non-stained classes are estimated using the nonparametric histogram method. The stained pixels are further processed to extract features (histogram, Hu moments, relative shape measurements, colour auto-correlogram) for a parasite/non-parasite classifier. A distance weighted Knearest neighbour classifier is trained with the extracted features and a detailed performance comparison is presented. We show that our method achieves 74% sensitivity, 98% specificity, 88% positive prediction value, and 95% negative prediction value for the parasite detection.