Machine aided malaria parasitemia detection in Giemsa-stained thin blood smears
Machine aided malaria parasitemia detection in Giemsa-stained thin blood smears
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DOI：
10.1007/s00521-016-2474-6
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发表时间：
2018-02-01
影响因子：
6
通讯作者：
Al-Ghamdi, Jarallah Saleh
中科院分区：
文献类型：
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作者：
Abbas, Naveed;Saba, Tanzila;Al-Ghamdi, Jarallah Saleh
疟疾寄生虫血症是对血液中寄生虫的定量测量，以分级感染程度。光学显微镜检查是用于检查血液中寄生虫血症定量的最知名的方法。疟疾寄生虫血症的视觉量化是费力、耗时和主观的。尽管自动化该过程是一个很好的解决方案，但由于与正常红细胞形态的偏离，现有技术无法评估相同的病例，例如贫血和血红蛋白病。本研究的主要目的是使用各种计算机视觉技术检查染色的薄血涂片的显微图像，根据独立因素（红细胞形态）对疟疾寄生虫血症进行分级。该方法基于归纳法，通过高斯混合模型（GMM）的自适应算法对疟原虫进行颜色分割。通过距离变换和局部极大值分解红细胞的遮挡，提高了红细胞的量化精度。此外，已对感染和未感染的RBC进行分类以适当地分级寄生虫血症。训练和评估已经在图像数据集上相对于地面真实数据进行，以98%的灵敏度和97%的特异性确定感染程度。实验证明，该方案的准确性和效率的上下文中被自动化，超过其他国家的最先进的计划。此外，本研究还讨论了独立因素（红细胞形态）的过程。最终，这可以被认为是大规模检查中疟疾寄生虫血症量化的低成本解决方案。
Malaria parasitemia is the quantitative measurement of the parasites in the blood to grade the degree of infection. Light microscopy is the most well-known method used to examine the blood for parasitemia quantification. The visual quantification of malaria parasitemia is laborious, time-consuming and subjective. Although automating the process is a good solution, the available techniques are unable to evaluate the same cases such as anemia and hemoglobinopathies due to deviation from normal RBCs' morphology. The main aim of this research is to examine the microscopic images of stained thin blood smears using a variety of computer vision techniques, grading malaria parasitemia on independent factors (RBC's morphology). The proposed methodology is based on inductive approach, color segmentation of malaria parasites through adaptive algorithm of Gaussian mixture model (GMM). The quantification accuracy of RBCs is improved, splitting the occlusions of RBCs with distance transform and local maxima. Further, the classification of infected and non-infected RBCs has been made to properly grade parasitemia. The training and evaluation have been carried out on image dataset with respect to ground truth data, determining the degree of infection with the sensitivity of 98% and specificity of 97%. The accuracy and efficiency of the proposed scheme in the context of being automatic were proved experimentally, surpassing other state-of-the-art schemes. In addition, this research addressed the process with independent factors (RBCs' morphology). Eventually, this can be considered as low-cost solutions for malaria parasitemia quantification in massive examinations.