Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination.
Mobile-Based Analysis of Malaria-Infected Thin Blood Smears: Automated Species and Life Cycle Stage Determination.
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DOI：
10.3390/s17102167
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发表时间：
2017-09-21
期刊：
影响因子：
--
通讯作者：
Cardoso JS
中科院分区：
文献类型：
--
作者：
Rosado L;da Costa JMC;Elias D;Cardoso JS
显微镜检查一直是疟疾诊断的支柱，在可以保持其质量的情况下是推荐的检查方法。然而，对训练有素的人员和足够设备的需求限制了其在疟疾流行地区的可用性和可及性。随着疟疾控制计划扩大基于寄生虫的诊断且患病率下降，人们越来越需要快速、准确、方便的诊断工具。本文提出了一种图像处理和分析方法，使用监督分类来评估疟疾寄生虫的存在并确定吉姆萨染色薄血涂片中的物种和生命周期阶段。主要的差异化因素是使用专门通过低成本且易于使用的工具（例如智能手机）获取的显微图像，使用由经验丰富的寄生虫学家手动注释的 566 张图像的数据集。这项工作考虑了八种不同的物种阶段组合，自动检测性能的灵敏度为 73.9% 至 96.2%，特异性为 92.6% 至 99.3%。这些有希望的结果证明了使用这种方法作为传统显微镜检查的有效替代方法的潜力，具有可比的检测性能和可接受的计算时间。
Microscopy examination has been the pillar of malaria diagnosis, being the recommended procedure when its quality can be maintained. However, the need for trained personnel and adequate equipment limits its availability and accessibility in malaria-endemic areas. Rapid, accurate, accessible diagnostic tools are increasingly required, as malaria control programs extend parasite-based diagnosis and the prevalence decreases. This paper presents an image processing and analysis methodology using supervised classification to assess the presence of malaria parasites and determine the species and life cycle stage in Giemsa-stained thin blood smears. The main differentiation factor is the usage of microscopic images exclusively acquired with low cost and accessible tools such as smartphones, a dataset of 566 images manually annotated by an experienced parasilogist being used. Eight different species-stage combinations were considered in this work, with an automatic detection performance ranging from 73.9% to 96.2% in terms of sensitivity and from 92.6% to 99.3% in terms of specificity. These promising results attest to the potential of using this approach as a valid alternative to conventional microscopy examination, with comparable detection performances and acceptable computational times.
影响因子：
3.7
作者：
Yang D;Subramanian G;Duan J;Gao S;Bai L;Chandramohanadas R;Ai Y
通讯作者：
Ai Y
影响因子：
--
作者：
Rosado, Luis;Correia da Costa, Jose M.;Cardoso, Jaime S.
通讯作者：
Cardoso, Jaime S.
通过均值漂移滤波预处理优化分割算法
影响因子：
4.8
作者：
Wang, Leiguang;Liu, Guoying;Dai, Qinling
通讯作者：
Dai, Qinling