Kernel density estimation and intrinsic alignment for shape priors in level set segmentation
Kernel density estimation and intrinsic alignment for shape priors in level set segmentation
复制标题
DOI：
10.1007/s11263-006-7533-5
复制
发表时间：
2006-09-01
影响因子：
19.5
通讯作者：
Soatto, Stefano
中科院分区：
文献类型：
--
作者：
Cremers, Daniel;Osher, Stanley J.;Soatto, Stefano
在本文中，我们做了两个领域的水平集图像分割的贡献。首先，我们提出了水平集函数空间上的形状相异性测度，这些水平集函数在一定的变换群作用下是解析不变的。不变性是通过演化水平集函数的内在注册获得的。与现有的水平集框架中的不变性方法相比，这种封闭形式的解决方案消除了迭代优化显式姿态参数的需要。得到的形状梯度是更准确的，因为它考虑到了边界变化对物体的pose.Second，基于这些不变的形状相异性措施，我们提出了一个统计形状先验，它允许准确地编码多个相当不同的训练形状。这种先验构成了核密度估计到水平集域的扩展。与常用的高斯分布相比，这种非参数密度估计适合于model aribtrary distributions.We证明了这种多模态形状先验应用于分割和跟踪视频序列中部分被遮挡的行走的人的优点，以及心脏超声图像中左心室的分割。我们给定量的结果分割精度和依赖的分割结果的训练形状的数量。
In this paper, we make two contributions to the field of level set based image segmentation. Firstly, we propose shape dissimilarity measures on the space of level set functions which are analytically invariant under the action of certain transformation groups. The invariance is obtained by an intrinsic registration of the evolving level set function. In contrast to existing approaches to invariance in the level set framework, this closed-form solution removes the need to iteratively optimize explicit pose parameters. The resulting shape gradient is more accurate in that it takes into account the effect of boundary variation on the object's pose.Secondly, based on these invariant shape dissimilarity measures, we propose a statistical shape prior which allows to accurately encode multiple fairly distinct training shapes. This prior constitutes an extension of kernel density estimators to the level set domain. In contrast to the commonly employed Gaussian distribution, such nonparametric density estimators are suited to model aribtrary distributions.We demonstrate the advantages of this multi-modal shape prior applied to the segmentation and tracking of a partially occluded walking person in a video sequence, and on the segmentation of the left ventricle in cardiac ultrasound images. We give quantitative results on segmentation accuracy and on the dependency of segmentation results on the number of training shapes.