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A Fuzzy Connectivity Tree for Hierarchical Extraction of Venous Structures

机译:一种模糊连接树,用于静脉结构的分层提取

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In Magnetic Resonance Angiography (MRA), blood vessels show very high grey levels resulting from the use of a contrast agent. This leads to develop a formula, named β-connectedness, derived from fuzzy-connectedness theory [1],[2]. β-connectedness is described here as a specific case of more general χ-connectedness [3], for it allows one to improve the tracking of bright structures and, consequently, vessel extraction. In the computation of fuzzy connectedness, a widely used approach is based on an adaptive growing mechanism that follows the best paths starting from a reference seed point. As a consequence, a hierarchical tree is generated. Contrary to the aforesaid approach, here we propose to exploit such a tree information and we define the connectivity level as an additional parameter useful in the analysis of image data. The method presented in the paper deals with a new kind of connectivity that depends on the position of each pixel in the growing tree. The detection of fine structures is thus improved, as demonstrated by preliminary results on 3D MRA volumes.
机译:在磁共振血管造影(MRA)中,血管显示出造影剂的非常高的灰度水平。这导致开发出来自模糊连接理论的命名为β关联的公式[1],[2]。这里描述β连接性作为更一般的χ连通性[3]的具体情况,因为它允许一个用于改善亮结构的跟踪,从而提取血管提取。在计算模糊连接度的计算中,广泛使用的方法是基于自适应生长机制,其遵循从参考种子点开始的最佳路径。结果,生成分层树。与上述方法相反,我们建议利用这样的树信息,我们将连接级别定义为可用于分析图像数据的附加参数。本文中呈现的方法涉及一种新的连接,这取决于生长树中每个像素的位置。因此改善了细结构的检测,正如3D MRA卷上的初步结果所证明的那样。

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