Armato S G, Giger M L, Moran C J, Blackburn J T, Doi K, MacMahon H
Department of Radiology, University of Chicago, IL 60637, USA.
Radiographics. 1999 Sep-Oct;19(5):1303-11. doi: 10.1148/radiographics.19.5.g99se181303.
Helical computed tomography (CT) is the most sensitive imaging modality for detection of pulmonary nodules. However, a single CT examination produces a large quantity of image data. Therefore, a computerized scheme has been developed to automatically detect pulmonary nodules on CT images. This scheme includes both two- and three-dimensional analyses. Within each section, gray-level thresholding methods are used to segment the thorax from the background and then the lungs from the thorax. A rolling ball algorithm is applied to the lung segmentation contours to avoid the loss of juxtapleural nodules. Multiple gray-level thresholds are applied to the volumetric lung regions to identify nodule candidates. These candidates represent both nodules and normal pulmonary structures. For each candidate, two- and three-dimensional geometric and gray-level features are computed. These features are merged with linear discriminant analysis to reduce the number of candidates that correspond to normal structures. This method was applied to a 17-case database. Receiver operating characteristic (ROC) analysis was used to evaluate the automated classifier. Results yielded an area under the ROC curve of 0.93 in the task of classifying candidates detected during thresholding as nodules or nonnodules.
螺旋计算机断层扫描（CT）是检测肺结节最敏感的成像方式。然而，单次CT检查会产生大量图像数据。因此，已开发出一种计算机化方案来自动检测CT图像上的肺结节。该方案包括二维和三维分析。在每个切片内，使用灰度阈值法从背景中分割出胸部，然后从胸部中分割出肺部。将滚球算法应用于肺分割轮廓以避免胸膜旁结节的丢失。对肺部体积区域应用多个灰度阈值以识别结节候选物。这些候选物既包括结节也包括正常肺部结构。对于每个候选物，计算二维和三维几何及灰度特征。这些特征与线性判别分析相结合以减少对应于正常结构的候选物数量。该方法应用于一个包含17个病例的数据库。使用受试者操作特征（ROC）分析来评估自动分类器。在将阈值化过程中检测到的候选物分类为结节或非结节的任务中，结果得出ROC曲线下面积为0.93。
