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
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%.
|Research Community:||University of Westminster > Electronics and Computer Science, School of|
|Deposited On:||08 Dec 2009 12:37|
|Last Modified:||08 Dec 2009 12:37|
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