Computerized detection of pulmonary nodules on CT scans
Computerized detection of pulmonary nodules on CT scans
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DOI：
10.1148/radiographics.19.5.g99se181303
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发表时间：
1999-09-01
期刊：
影响因子：
5.5
通讯作者：
MacMahon, H
中科院分区：
文献类型：
--
作者：
Armato, SG;Giger, ML;MacMahon, H
螺旋计算机断层扫描（CT）是检测肺结节最灵敏的成像方式，但单次CT检查会产生大量的图像数据。因此，开发了一种计算机方案来自动检测CT图像上的肺结节，该方案包括二维和三维分析。在每个部分中。使用灰度阈值法将胸腔从背景中分离出来，然后将肺从胸腔中分离出来。肺分割轮廓采用滚动球算法，避免胸膜旁结节丢失；肺体积区域采用多个灰度阈值识别候选结节。这些候选者既代表结节，也代表不正常的肺结构。对于每个候选点，计算二维和三维几何特征和灰度特征。这些特征与线性判别分析合并，以减少对应于正常结构的候选数量。该方法应用于17例数据库。使用受试者工作特征（ROC）分析来评估自动分类器，结果在将阈值期间检测到的候选者分类为结节或非结节的任务中，ROC曲线下面积为0.93。
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 nor mal 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.