基于模型的三维血管多尺度检测

K. Krissian, G. Malandain, N. Ayache, Régis Vaillant, Y. Trousset
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引用次数: 18

摘要

提出了一种从脑三维血管造影中分割血管的新方法。作者的方法是基于血管模型,并使用多尺度分析来提取动脉瘤周围的血管网络。作者的模型允许他们根据Hessian矩阵的特征值选择一个准则,用于选择靠近容器中心的感兴趣点子集。它还允许他们为单尺度响应的a/ spl γ /-归一化选择一个好的参数。在一个尺度上的响应是通过沿圆周积分强度在径向方向上的一阶导数得到的。一旦获得了多尺度响应,作者就会创建一个光滑的血管骨架,并结合MIP或体积渲染来增强其可视化。这种方法已经在大量的大脑三维图像上进行了测试,效果非常好。检测到不同大小和造影剂的血管,具有显著的稳健性,大多数连接处保存完好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Model based multiscale detection of 3D vessels
Presents a new approach to segment vessels from 3D angiography of the brain. The authors' approach is based on a vessel model and uses a multiscale analysis in order to extract the vessel network surrounding an aneurysm. The authors' model allows them to choose a criterion based on the eigenvalues of the Hessian matrix for selecting a subset of interesting points near the vessel center. It also allows them to choose a good parameter for a /spl gamma/-normalization of the single scale response. The response at one scale is obtained by integrating along a circle the first derivative of the intensity in the radial direction. Once the multiscale response is obtained, the authors create a smoothed skeleton of the vessels combined with a MIP or a volume rendering to enhance their visualization. The method has been tested on a large variety of 3-D images of the brain, with excellent results. Vessels of various size and contrast are detected with a remarkable robustness, and most junctions are preserved.
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