Malaria parasite detection in peripheral blood images

F. Boray Tek, Andrew G. Dempster, Izzet Kale

Research output: Contribution to conferencePaperpeer-review

76 Citations (Scopus)


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 using 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 non-parametric 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 K-nearest neighbour classifier is trained with the extracted features and a detailed performance comparison is presented. Our method achieves 74% sensitivity, 98% specificity, 88% positive prediction, and 95% negative prediction values for the parasite detection.

Original languageEnglish
Number of pages10
Publication statusPublished - 2006
Externally publishedYes
Event2006 17th British Machine Vision Conference, BMVC 2006 - Edinburgh, United Kingdom
Duration: 4 Sept 20067 Sept 2006


Conference2006 17th British Machine Vision Conference, BMVC 2006
Country/TerritoryUnited Kingdom


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