Pulmonary embolism (PE) and other pulmonary vascular diseases, have been found associated with the changes in arterial morphology. To detect arterial changes, we propose a novel, fully automatic method that can extract pulmonary arterial tree in computed tomographic pulmonary angiography (CTPA) images. The approach is based on the fuzzy connectedness framework, combined with 3D vessel enhancement and Harris Corner detection to achieve accurate segmentation. The effectiveness and robustness of the method is validated in clinical datasets consisting of 10 CT angiography scans (6 without PE and 4 with PE). The performance of our method is compared with manual classification and machine learning method based on random forest. Our method achieves a mean accuracy of 92% when compared to manual reference, which is higher than the 89% accuracy achieved by machine learning. This performance of the segmentation for pulmonary arteries may provide a basis for the CAD application of PE.

已发现肺栓塞（PE）和其他肺血管疾病与动脉形态的变化有关。为了检测动脉变化，我们提出了一种新颖的全自动方法，该方法可以在计算机断层扫描肺血管造影（CTPA）图像中提取肺动脉树。该方法基于模糊连接框架，结合3D血管增强和Harris角检测以实现准确的分割。该方法的有效性和稳健性已在由10次CT血管造影扫描（6次不使用PE和4次使用PE）组成的临床数据集中得到了验证。将我们的方法的性能与基于随机森林的人工分类和机器学习方法进行了比较。与手动参考相比，我们的方法可实现92％的平均准确度，高于机器学习达到的89％的准确性。肺动脉分割的这种性能可以为PE的CAD应用提供基础。