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首页> 外文会议>IEEE International Symposium on Biomedical Imaging: From Nano to Macro >Automatic global vessel segmentation and catheter removal using local geometry information and vector field integration
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Automatic global vessel segmentation and catheter removal using local geometry information and vector field integration

机译：使用局部几何信息和矢量场积分自动进行全局血管分割和导管去除

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Vessel enhancement and segmentation aim at (binary) per-pixel segmentation considering certain local features as probabilistic vessel indicators. We propose a new methodology to combine any local probability map with local directional vessel information. The resulting global vessel segmentation is represented as a set of discrete streamlines populating the vascular structures and providing additional connectivity and geometric shape information. The streamlines are computed by numerical integration of the directional vector field that is obtained from the eigenanalysis of the local Hessian indicating the local vessel direction. The streamline representation allows for sophisticated post-processing techniques using the additional information to refine the segmentation result with respect to the requirements of the particular application such as image registration. We propose different post-processing techniques for hierarchical segmentation, centerline extraction, and catheter removal to be used for X-ray angiograms. We further demonstrate how the global approach is able to significantly improve the segmentation compared to conventional local Hessian-based approaches.
机译：血管增强和分割的目标是将某些局部特征作为概率血管指示符，以（二进制）每像素分割。我们提出了一种将任何局部概率图与局部定向船只信息相结合的新方法。最终的整体血管分割表示为一组离散的流线，这些流线填充了血管结构并提供了附加的连通性和几何形状信息。通过对方向矢量场进行数值积分来计算流线，该方向矢量场是从表示本地船只方向的本地Hessian特征分析中获得的。流线型表示允许使用附加信息来进行复杂的后处理技术，以根据特定应用程序（例如图像配准）的要求优化分割结果。我们提出了用于分层分割，中心线提取和导管去除的不同后处理技术，以用于X射线血管造影。我们进一步证明，与传统的基于本地Hessian的方法相比，全局方法如何能够显着改善细分。

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