Parasite detection and identification for automated thin blood film malaria diagnosis
Parasite detection and identification for automated thin blood film malaria diagnosis
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DOI：
10.1016/j.cviu.2009.08.003
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发表时间：
2010-01-01
影响因子：
4.5
通讯作者：
Kale, Izzet
中科院分区：
文献类型：
--
作者：
Tek, F. Boray;Dempster, Andrew G.;Kale, Izzet
本文研究了在Giemsan染色的血膜标本图像中疟疾寄生虫的自动检测和识别，Giemsa染色不仅突出了疟疾寄生虫，而且还突出了白细胞、血小板和人工制品。我们提出了一个完整的框架来提取这些染色的结构，确定它们是否是寄生虫，并识别感染物种和生命周期阶段。我们将物种和生命周期阶段的识别作为多类分类问题来研究，其中我们比较了三种不同的分类方案，并经验地证明了该检测方法的有效性。物种和生命周期阶段的任务可以在联合分类中执行，也可以扩展到二元检测中执行。提出的二元寄生虫检测器可以在0.1%的寄生虫血症下工作，没有任何错误检测，并且在低至0.01%的水平上错误检测少于10次。(C)2009 Elsevier Inc.保留所有权利。
This paper investigates automated detection and identification of malaria parasites in images of Giemsastained 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%. (C) 2009 Elsevier Inc. All rights reserved.