Automatic identification of Mycobacterium tuberculosis by Gaussian mixture models
Automatic identification of Mycobacterium tuberculosis by Gaussian mixture models
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DOI：
10.1111/j.1365-2818.2006.01610.x
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发表时间：
2006-08-01
影响因子：
2
通讯作者：
Desco, M.
中科院分区：
文献类型：
--
作者：
Forero, M. G.;Cristobal, G.;Desco, M.
结核病和其他种类的分枝杆菌病是严重的疾病，早期诊断对疾病控制至关重要。痰液样本分析是用于芽孢杆菌检测的常用手动技术，但目前的样本分析技术耗时、非常繁琐、特异性差并且需要训练有素的人员。图像处理和模式识别技术是改进样本人工筛选的适当工具。在这里，我们提出了一种新的技术，痰液图像分析，结合不变的形状特征和彩色通道阈值。从编辑的芽孢杆菌数据集中提取一些特征描述符来表征它们的形状。它们通过使用高斯混合模型表示和最小误差贝叶斯分类程序的最后识别阶段进行统计表示。该技术构成了朝向自动化过程和提供高特异性的一个步骤。
Tuberculosis and other kinds of mycobacteriosis are serious illnesses for which early diagnosis is critical for disease control. Sputum sample analysis is a common manual technique employed for bacillus detection but current sample-analysis techniques are time-consuming, very tedious, subject to poor specificity and require highly trained personnel. Image-processing and pattern-recognition techniques are appropriate tools for improving the manual screening of samples. Here we present a new technique for sputum image analysis that combines invariant shape features and chromatic channel thresholding. Some feature descriptors were extracted from an edited bacillus data set to characterize their shape. They were statistically represented by using a Gaussian mixture model representation and a minimal error Bayesian classification procedure was employed for the last identification stage. This technique constitutes a step towards automating the process and providing a high specificity.