Parasite detection and identification for automated thin blood film malaria diagnosis

Authors:
F. Boray Tek;Andrew G. Dempster;İzzet Kale
Affiliations:
University of Westminster, Applied DSP and VLSI Research Group, London, UK;University of New South Wales, School of Surveying and Spatial Information Systems, Sydney, Australia;University of Westminster, Applied DSP and VLSI Research Group, London, UK
Venue:
Computer Vision and Image Understanding
Year:
2010

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Abstract

This paper investigates automated detection and identification of malaria parasites in images of Giemsa-stained thin blood film specimens. The Giemsa stain highlights not only the malaria parasites but also the white blood cells, platelets, and artefacts. We propose a complete framework to extract these stained structures, determine whether they are parasites, and identify the infecting species and life-cycle stages. We investigate species and life-cycle-stage identification as multi-class classification problems in which we compare three different classification schemes and empirically show that the detection, species, and life-cycle-stage tasks can be performed in a joint classification as well as an extension to binary detection. The proposed binary parasite detector can operate at 0.1% parasitemia without any false detections and with less than 10 false detections at levels as low as 0.01%.