Segmentation and Quantification of Blood Vessels in 3D Images using a Right Generalized Cylinder State Model
Segmentation and Quantification of Blood Vessels in 3D Images using a Right Generalized Cylinder State Model
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DOI：
10.1109/icip.2006.312770
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发表时间：
2006-10
期刊：
影响因子：
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通讯作者：
L. Florez-Valencia;J. Azencot;Fabrice Vincent;M. Orkisz;I. Magnin
中科院分区：
文献类型：
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作者：
L. Florez-Valencia;J. Azencot;Fabrice Vincent;M. Orkisz;I. Magnin
提出了一种基于右广义圆柱状态模型（RGC-sm）的血管分割与定量方法。RGC-sm模型包括一个与一堆等高线相关联的曲线轴。轴由状态向量（局部曲率、扭转和旋转）描述。轮廓由傅里叶级数分解来描述。挑战在于自动调整该模型以适应三维血管数据（分割）。通过对实际医疗数据的拟合，可以得到状态模型参数和量化措施。我们对一组校准的幻影和临床数据集（颈动脉3D-CTA和主动脉3D-MRA）的定性结果进行了定量分析。
We present a vascular segmentation and quantification method based on the right generalized cylinder state model (RGC-sm). The RGC-sm model includes a curvilinear axis associated to a stack of contours. The axis is described by a state vector (local curvature, torsion and rotation). The contours are described by a Fourier series decomposition. The challenge is to automatically adjust this model to 3D vascular data (segmentation). By fitting the synthetic model to the actual medical data, it is possible to get the state model parameters and quantification measures. We present quantitative results on a set of calibrated phantoms and qualitative results on clinical datasets (carotid 3D-CTA and aortic 3D-MRA).