Classification of Mycobacterium tuberculosis in images of ZN-stained sputum smears.
Classification of Mycobacterium tuberculosis in images of ZN-stained sputum smears.
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DOI：
10.1109/titb.2009.2028339
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发表时间：
2010-07
期刊：
影响因子：
--
通讯作者：
Douglas TS
中科院分区：
文献类型：
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作者：
Khutlang R;Krishnan S;Dendere R;Whitelaw A;Veropoulos K;Learmonth G;Douglas TS
在低收入和中等收入国家，结核病（TB）筛查主要集中在显微镜上。我们提出的方法，用于自动识别结核分枝杆菌的Ziehl-Neelsen（ZN）染色的痰涂片使用明场显微镜获得的图像。我们分割候选芽孢杆菌对象使用两个类的像素分类器的组合。通过Hausdorff距离和修正的威廉姆斯指数判断，该算法产生的结果与手动分割吻合得很好。几何变换不变特征的提取和特征子集的选择和Fisher变换的特征集的优化如下。最后，对不同的两类目标分类器进行了比较。所有测试的分类器的灵敏度和特异性是95%以上的识别由Fisher变换的功能表示的芽孢杆菌对象。我们的研究结果可用于减少技术人员参与结核病筛查，特别是在结核病负担高的国家的实验室，通常，ZN而不是金胺染色的痰涂片是首选的方法。
Screening for tuberculosis (TB) in low- and middle-income countries is centered on the microscope. We present methods for the automated identification of Mycobacterium tuberculosis in images of Ziehl–Neelsen (ZN) stained sputum smears obtained using a bright-field microscope. We segment candidate bacillus objects using a combination of two-class pixel classifiers. The algorithm produces results that agree well with manual segmentations, as judged by the Hausdorff distance and the modified Williams index. The extraction of geometric-transformation-invariant features and optimization of the feature set by feature subset selection and Fisher transformation follow. Finally, different two-class object classifiers are compared. The sensitivity and specificity of all tested classifiers is above 95% for the identification of bacillus objects represented by Fisher-transformed features. Our results may be used to reduce technician involvement in screening for TB, and would be particularly useful in laboratories in countries with a high burden of TB, where, typically, ZN rather than auramine staining of sputum smears is the method of choice.