Point-based non-rigid surface registration with accuracy estimation
Point-based non-rigid surface registration with accuracy estimation
复制标题
具有精度估计的基于点的非刚性表面配准
DOI：
10.1109/cvpr.2010.5540181
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发表时间：
2010
期刊：
影响因子：
--
通讯作者：
Wataru Watanabe
中科院分区：
文献类型：
--
作者：
H. Hontani;Wataru Watanabe
本文提出了一种新的非刚性表面之间的表面模型和内部器官的表面在给定的三维医学图像配准的方法。表面用一组特征点表示，特征点的位置由图形模型表示。为了构造表示，一组对应的点分布在每个训练表面上的基于熵的粒子系统。从这些对应的点，我们估计每个特征点的位置的概率密度，每个特征点周围的局部图像模式的条件概率分布，以及两个相邻特征点之间的相对位置的概率分布。当一个新的图像是给定的，这些密度被用于估计每个特征点的位置，通过一个非参数的信念传播。该方法不仅可以估计特征点的位置，而且可以估计它们在给定图像中的条件边缘分布。从真实的X-CT图像中得到的一些实验结果显示了它的性能。
This article presents a new method for non-rigid surface registration between a surface model and a surface of an internal organ in a given 3D medical image. The surface is represented with a set of feature points, of which locations are represented by a graphical model. For constructing the representation, a set of corresponding points is distributed on each of training surfaces based on an entropy-based particle system. From these corresponding points, we estimate probability densities of the location of each feature point, the conditional probability distribution of the local image pattern around each feature point, and the probability distributions of relative positions between two neighboring feature points. When a new image is given, these densities are used for estimating the location of each feature point by means of a non-parametric belief propagation. The proposed method can estimate not only the locations of the feature points but also their conditional marginal distributions in a given image. Some experimental results obtained from real X-CT images are presented to show its performance.