目的 计算机断层血管造影术（CTA）是冠脉疾病诊疗过程中最为常用的成像方法之一。然而造影图像中冠脉与其周边组织的灰度分布较为接近，冠脉整体结构的识别较为困难。为此提出一种基于方向聚类的冠脉结构跟踪提取方法。方法 该方法首先使用闭运算及灰度拉伸对影像进行预处理，然后在选定的种子点构造球形算子，该球形算子由多对方向相反且从球心指向球壳的射线组成，通过对多组数据进行分析得到血管在球形算子内部的灰度差异率特征，对灰度差异率特征曲线构造2维凸包可得到射线与血管壁的交点，以此获得血管段的分割结果以及指向血管走势的方向向量的集合，应用最小距离层次聚类法对向量集合进行分类得到跟踪方向，遍历整条血管完成对冠状动脉的分割。结果 实验结果表明，本文方法能够获得较为精确的冠脉提取结果，与近年流行算法相比在临床有效血管段的跟踪上具有一定优势。能够达到0.39 mm的跟踪精度。结论 本文方法的优势在于所提出的球形算子对不同形态的3维血管具有较好的适应性和鲁棒性。用于向量分类的层次聚类方法无需训练，算法自动化程度高，且分类较为准确。血管跟踪提取方法精度较高。

Objective Computed tomography angiography (CTA) is one of the most widely used imaging techniques to diagnose coronary artery disease. However, the similarities of the gray scale distributions of both coronary arteries and surrounding tissues make the recognition of vascular structures difficult. This study proposes a coronary artery tracking method based on direction clustering. Method Vessel structures are enhanced by integrating the closing operation and adaptive grayscale stretching. A spherical operator is proposed for the detection of vascular boundaries. The operator is constructed by placing direction rays at the center of the extraction points. The intensity difference ratios of adjacent sampling points are extracted based on the intensity distribution on the rays. Vascular boundaries are then extracted and refined through convex hull optimization. Finally, hierarchical clustering is employed to extract the direction vector of the vascular structure. Result demonstrate that the proposed method can accurately extract the coronary artery from CTA images. The extraction accuracy can be reached to 0.39 mm. Conclusion The designed spherical operator can be well adaptive to different kinds of 3D vessels. And the hierarchical clustering method that is used for the director vector classification don't need the training. The proposed method has high automaticity and accuracy.