A direct approach to estimating surfaces in tomographic data
A direct approach to estimating surfaces in tomographic data
复制标题
估计断层扫描数据中表面的直接方法
DOI：
10.1016/s1361-8415(02)00082-8
复制
发表时间：
2002
影响因子：
10.9
通讯作者：
V. Elangovan
中科院分区：
文献类型：
--
作者：
R. Whitaker;V. Elangovan
在理想情况下，氡变换的逆是可计算的，并且测量的投影序列足以获得体积密度的准确估计。在正弦图数据不完整的情况下，Radon变换是不可逆的，并且试图重建灰度密度值会导致重建伪影，这会破坏后续处理的有效性。本文提出了一种直接的方法来分割不完整的层析数据。该策略是对数据施加一个相当简单的模型，并将分割视为估计密度均匀的两种物质之间的界面的问题。通过同时使表面模型变形并更新密度参数来实现分割，以便实现投影模型与测量的正弦图之间的最佳拟合。变形是用水平集表面模型实现的，以输入数据的分辨率计算。相对于以前的工作，本文作出了一些贡献。首先是一个适当的推导变形的表面模型，移动根据梯度下降的似然性措施。我们还提出了一系列的计算创新，使这种直接的表面拟合方法可行的国家的最先进的计算机。另一个贡献是证明这种方法的有效性下约束的层析成像问题，使用模拟和真实的数据集。
Under ideal circumstances, the inverse of the radon transform is computable, and sequences of measured projections are sufficient to obtain accurate estimates of volume densities. In situations where the sinogram data is incomplete, the radon transform is noninvertable, and attempts to reconstruct greyscale density values result in reconstruction artifacts that can undermine the effectiveness of subsequent processing. This paper presents a direct approach to the segmentation of incomplete tomographic data. The strategy is to impose a fairly simple model on the data, and treat segmentation as a problem of estimating the interface between two substances of somewhat homogeneous density. The segmentation is achieved by simultaneously deforming a surface model and updating density parameters in order to achieve a best fit between the projected model and the measured sinograms. The deformation is implemented with level-set surface models, calculated at the resolution of the input data. Relative to previous work, this paper makes several contributions. First is a proper derivation of the deformation of surface models that move according to a gradient descent on a likelihood measure. We also present a series of computational innovations that make this direct surface-fitting approach feasible with state-of-the-art computers. Another contribution is the demonstration of the effectiveness of this approach on under-constrained tomographic problems, using both simulated and real datasets.