Artery-vein separation via MRA - An image processing approach
Artery-vein separation via MRA - An image processing approach
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DOI：
10.1109/42.938238
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发表时间：
2001-08-01
影响因子：
10.6
通讯作者：
Odhner, D
中科院分区：
文献类型：
--
作者：
Lei, TH;Udupa, JK;Odhner, D
本文提出了一种用于从背景和其他杂波中分离血管以及用于分离对比增强磁共振血管造影（CE-MRA）图像数据中的动脉和静脉的近自动过程，以及用于血管结构的三维可视化的最佳方法。分离过程利用模糊连接对象描绘原理和算法。该分离过程的第一步是通过绝对模糊连通性从背景和其他杂波中分割整个血管结构。第二步是通过迭代的相对模糊连通度在整个血管结构中分离动脉和静脉。在CE-MRA图像中的动脉和静脉内指定种子体素之后，动脉和静脉的较大方面的小区域在初始迭代中被分离，并且动脉和静脉的进一步详细方面被包括在稍后的迭代中。在每一次迭代中，动脉和静脉之间的竞争，以抓住成员的每个体素在血管结构的基础上的相对强度的连通性的体素在动脉和vein.This方法已被实现在一个软件包中的常规使用在临床设置和测试133 CE-MRA研究的骨盆区域和两个研究的颈动脉系统从六个不同的医院。在所有研究中，统一的参数设置产生了正确的动静脉分离。与手动分割/分离相比，我们的算法能够分离高阶分支，因此在分割的血管结构中产生了更多的细节。每次研究所需的操作员和计算机总时间平均约为4.5分钟。迄今为止，该技术似乎是唯一可常规应用于动脉和静脉分离的图像处理方法。
This paper presents a near-automatic process for separating vessels from background and other clutter as well as for separating arteries and veins in contrast-enhanced magnetic resonance angiographic (CE-MRA) image data, and an optimal method for three-dimensional visualization of vascular structures. The separation process utilizes fuzzy connected object delineation principles and algorithms. The first step of this separation process is the segmentation of the entire vessel structure from the background and other clutter via absolute fuzzy connectedness. The second step is to separate artery from vein within this entire vessel structure via iterative relative fuzzy connectedness. After seed voxels are specified inside artery and vein in the CE-MRA image, the small regions of the bigger aspects of artery and vein are separated in the initial iterations, and further detailed aspects of artery and vein are included in later iterations. At each iteration, the artery and vein compete among themselves to grab membership of each voxel in the vessel structure based on the relative strength of connectedness of the voxel in the artery and vein.This approach has been implemented in a software package for routine use in a clinical setting and tested on 133 CE-MRA studies of the pelvic region and two studies of the carotid system from six different hospitals. In all studies, unified parameter settings produced correct artery-vein separation. When compared with manual segmentation/separation, our algorithms were able to separate higher order branches, and therefore produced vastly more details in the segmented vascular structure. The total operator and computer time taken per study is on the average about 4.5 min. To date, this technique seems to be the only image processing approach that can be routinely applied for artery and vein separation.