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中文摘要：
血管的中心路径提取是虚拟血管镜的重要组成部分,它提供了自动导航的路径.本文提出一种新的内窥路径生成方法,用改进L1中值算法对体素点云化的脑血管数据进行骨架的提取.首先,对核磁共振成像（Magnetic resonance imaging,MRI）增强血管数据进行基于统计的分割算法进行分割;其次,对推广的Roberts算子在体素空间分割出的单体素点边界进行体素点的点云化,生成点云模型;最后,在点云空间中运用基于法向信息的L1中值算法进行骨架提取.该过程克服了传统方法在体素中进行骨架提取时对数据缺失、孤点敏感的局限性,并且对下采样后的点云化数据提取的骨架效率高,骨架居中性较好,最终把骨架用作脑血管虚拟内窥的自动漫游路径,实现自动导航.
英文摘要：
Centerline extraction of curvilinear objects is a crucial component of virtual angioscopy because it provides path planning for automatic navigation. In this paper, we suggest a new idea to extract center path for point cloud data of cerebrovascular based on improved Ll-media algorithm. First, the vessel segmentation algorithm based on statistics is applied to enhanced cerebrovascular. Then the promoted Roberts operator is used to extract single-voxel edge to generate a point cloud model of the vessel. At last, the Ll-media algorithm based on point cloud normals is put forward to extract the wandering path. This process overcomes the limitations that the conventional wandering path extraction algorithms fail in outlier and missing data, improves efficiency and makes the wandering path closer to the medial axis of the vessel. The wandering path is the navigation path for angioscopy to achieve automatic navigation.
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