Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images
Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images
复制标题
DOI：
10.1109/42.929615
复制
发表时间：
2001-06-01
影响因子：
10.6
通讯作者：
Reinhardt, JM
中科院分区：
文献类型：
--
作者：
Hu, SY;Hoffman, EA;Reinhardt, JM
肺部 X 射线计算机断层扫描 (CT) 图像的分割是大多数肺部图像分析应用的先驱。本文提出了一种在三维 (3-D) 肺部 X 射线 CT 图像中识别肺部的全自动方法。该方法具有三个主要步骤。首先，通过灰度阈值处理从 CT 图像中提取肺部区域。然后，通过动态编程识别前、后连接处来分离左肺和右肺。最后，使用一系列形态学操作来平滑沿纵隔的不规则边界，以获得与手动分析一致的结果，其中仅将最中央的肺动脉排除在肺部区域之外。该方法通过处理来自八名正常受试者的 3-D CT 数据集进行了测试，每个正常受试者每两周成像 3 次，肺活量为 90%。我们通过将我们的自动方法与两位图像分析师手动追踪的边界进行比较来展示结果。对所有体积进行平均，计算机分析与人工分析之间的均方根差异为 0.8 像素（0.54 毫米）。三次扫描中组织含量的平均受试者内部变化为 2.75% +/- 2.29%（平均值 +/- 标准差）。
Segmentation of pulmonary X-ray computed tomography (CT) images is a precursor to most pulmonary image analysis applications. This paper presents a fully automatic method for identifying the lungs in three-dimensional (3-D) pulmonary X-ray CT images. The method has three main steps. First, the lung region is extracted from the CT images by gray-level thresholding. Then, the left and right lungs are separated by identifying the anterior and posterior junctions by dynamic programming. Finally, a sequence of morphological operations is used to smooth the irregular boundary along the mediastinum in order to obtain results consistent with those obtained by manual analysis, in which only the most central pulmonary arteries are excluded from the lung region.The method has been tested by processing 3-D CT data sets from eight normal subjects, each imaged three times at biweekly intervals with lungs at 90% vital capacity. We present results by comparing our automatic method to manually traced borders from two image analysts. Averaged over all volumes, the root mean square difference between the computer and human analysis is 0.8 pixels (0.54 mm). The mean intrasubject change in tissue content over the three scans was 2.75% +/- 2.29% (mean +/- standard deviation).