WestminsterResearch

Parasite detection and identification for automated thin blood film malaria diagnosis

Tek, F. Boray and Dempster, Andrew G. and Kale, Izzet (2010) Parasite detection and identification for automated thin blood film malaria diagnosis. Computer Vision and Image Understanding, 114 (1). pp. 21-32. ISSN 1077-3142

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Official URL: http://dx.doi.org/10.1016/j.cviu.2009.08.003

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%.

Item Type:Article
Research Community:University of Westminster > Electronics and Computer Science, School of
ID Code:7078
Deposited On:08 Dec 2009 12:37
Last Modified:08 Dec 2009 12:37

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