Forero M G, Cristóbal G, Desco M
School of Biosciences, University of Birmingham, UK.
J Microsc. 2006 Aug;223(Pt 2):120-32. doi: 10.1111/j.1365-2818.2006.01610.x.
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.
结核病和其他类型的分枝杆菌病是严重疾病，早期诊断对于疾病控制至关重要。痰液样本分析是用于检测杆菌的常见手工技术，但当前的样本分析技术耗时、非常繁琐、特异性差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手工筛查的合适工具。在此，我们提出一种用于痰液图像分析的新技术，该技术结合了不变形状特征和色彩通道阈值处理。从编辑后的杆菌数据集中提取了一些特征描述符以表征其形状。通过使用高斯混合模型表示对它们进行统计表示，并在最后识别阶段采用最小误差贝叶斯分类程序。该技术朝着实现自动化过程和提供高特异性迈出了一步。
