Shen Rui, Cheng Irene, Basu Anup
IEEE Trans Biomed Eng. 2010 Nov;57(11). doi: 10.1109/TBME.2010.2057509. Epub 2010 Jul 12.
Tuberculosis (TB) is a deadly infectious disease and the presence of cavities in the upper lung zones is a strong indicator that the disease has developed into a highly infectious state. Currently, the detection of TB cavities is mainly conducted by clinicians observing chest radiographs. Diagnoses performed by radiologists are labor intensive and very often there is insufficient healthcare personnel available, especially in remote communities. After assessing existing approaches, we propose an automated segmentation technique which takes a hybrid knowledge-based Bayesian classification approach to detect TB cavities automatically. We apply gradient inverse coefficient of variation (GICOV) and circularity measures to classify detected features and confirm true TB cavities. By comparing with non hybrid approaches and the classical active contour techniques for feature extraction in medical images, experimental results demonstrate that our approach achieves high accuracy with a low false positive rate in detecting TB cavities.
肺结核（TB）是一种致命的传染病，上肺区域出现空洞是该疾病已发展到高度传染状态的有力指标。目前，肺结核空洞的检测主要由临床医生通过观察胸部X光片来进行。放射科医生进行的诊断工作强度大，而且往往缺乏足够的医护人员，尤其是在偏远社区。在评估现有方法之后，我们提出了一种自动分割技术，该技术采用基于混合知识的贝叶斯分类方法来自动检测肺结核空洞。我们应用梯度变异反系数（GICOV）和圆形度测量来对检测到的特征进行分类，并确认真正的肺结核空洞。通过与非混合方法以及医学图像特征提取的经典活动轮廓技术进行比较，实验结果表明，我们的方法在检测肺结核空洞时具有高精度和低假阳性率。
