Tizon Xavier, Smedby Orjan
Center for Image Analysis, Uppsala, Sweden.
J Magn Reson Imaging. 2002 Apr;15(4):438-45. doi: 10.1002/jmri.10047.
To describe and present some preliminary results for a novel algorithm for segmentation with gray-scale connectedness as a means to separate arteries and veins in magnetic resonance angiography (MRA).
The proposed algorithm, SeparaSeed, uses the gray-scale degree of connectedness as a tool to find the zone surrounding each vessel, in order to split the original volume into its different vessel components. In contrast to traditional segmentation methods, no gray-scale information is lost in the process. The segmentation is performed in one step, resulting in a partition of the initial volume into a chosen number of regions of interest (ROIs). Finally, visualization is achieved by projecting the 3D vessel trees to 2D using the common maximum intensity projection (MIP). The algorithm was tested in two MRA data sets of the vessels of the pelvis acquired after injection of an intravascular contrast agent and in one data set of the vessels of the neck with gadolinium.
In all data sets, a large proportion of the venous signal was removed while preserving that of the arteries, thus improving visualization of the relevant vessels.
Separation of arteries and veins is feasible with the proposed algorithm with a moderate amount of interaction.
描述并展示一种基于灰度连通性的新型分割算法的一些初步结果，该算法用于在磁共振血管造影（MRA）中分离动脉和静脉。
所提出的SeparaSeed算法利用灰度连通度作为工具来找到围绕每条血管的区域，以便将原始体积分割成不同的血管成分。与传统分割方法不同，在此过程中不会丢失灰度信息。分割在一步中完成，从而将初始体积划分为选定数量的心感兴趣区域（ROI）。最后，通过使用常见的最大强度投影（MIP）将3D血管树投影到2D来实现可视化。该算法在注射血管内造影剂后获取的两个骨盆血管MRA数据集以及一个使用钆剂的颈部血管数据集中进行了测试。
在所有数据集中，大部分静脉信号被去除，同时保留了动脉信号，从而改善了相关血管的可视化。
使用所提出的算法并进行适度的交互，分离动脉和静脉是可行的。
