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首页> 外文会议>Image Processing pt.3; Progress in Biomedical Optics and Imaging; vol.8,no.31; Proceedings of SPIE-The International Society for Optical Engineering; vol.6512 pt.3 >Application of 3D Geometric Tensors for Segmenting Cylindrical Tree Structures from Volumetric Datasets
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Application of 3D Geometric Tensors for Segmenting Cylindrical Tree Structures from Volumetric Datasets

机译：3D几何张量在从体积数据集分割圆柱树结构中的应用

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摘要

Many diagnostic problems involve the assessment of vascular structures or bronchial trees depicted in volumetric datasets, but previous algorithms for segmenting cylindrical structures are not sufficiently robust for them to be widely applied clinically. Local geometric information that is of importance in segmentation consists of voxel values and their first and second derivatives. First derivatives can be generalized to the gradient and more generally the structure tensor, while the second derivatives can be represented by Hessian matrices. It is desirable to exploit both kinds of information, at the same time, in any voxel classification process, but few segmentation algorithms have attempted to do this. This project compares segmentation based on the structure tensor to that based on the Hessian matrix, and attempts to determine whether some combination of the two can demonstrate better performance than either individually. To compare performance in a situation where a gold standard exists, the methods were tested on simulated tree structures. We generated 3D tree structures with varying amounts of added noise, and processed them with algorithms based on the structure tensor, the Hessian matrix, and a combination of the two. We applied an orientation-sensitive filter to smooth the tensor fields. The results suggest that the structure tensor by itself is more effective in detecting cylindrical structures than the Hessian tensor, and the combined tensor is better than either of the other tensors.
机译：许多诊断问题涉及对体积数据集中描述的血管结构或支气管树的评估，但是以前的用于分割圆柱结构的算法不足以使它们在临床上得到广泛应用。在分割中很重要的局部几何信息包括体素值及其一阶和二阶导数。一阶导数可以概括为梯度，更一般而言，可以概括为结构张量，而二阶导数可以由Hessian矩阵表示。期望在任何体素分类过程中同时利用两种信息，但是很少有分割算法尝试这样做。该项目将基于结构张量的分割与基于Hessian矩阵的分割进行了比较，并试图确定两者的某种组合是否比单独的一种表现出更好的性能。为了在存在黄金标准的情况下比较性能，在模拟树结构上测试了这些方法。我们生成了具有变化的附加噪声量的3D树结构，并使用基于结构张量，Hessian矩阵以及二者的组合的算法对其进行了处理。我们应用了方向敏感的滤波器来平滑张量场。结果表明，结构张量本身比Hessian张量更有效地检测圆柱结构，并且组合张量比其他任何张量都好。

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