Statistical shape and appearance models without one-to-one correspondences
Statistical shape and appearance models without one-to-one correspondences
复制标题
没有一一对应的统计形状和外观模型
DOI：
10.1117/12.2043531
复制
发表时间：
2014
影响因子：
10.6
通讯作者：
H. Handels
中科院分区：
文献类型：
--
作者：
J. Ehrhardt;J. Krüger;H. Handels
一对一的对应关系是创建经典统计形状和外观模型的基础。同时，这些对应关系的识别是这种基于模型的方法的弱点。Hufnagel等人1提出了一种替代方法，使用对应概率代替统计形状模型的精确一对一对应。在这项工作中，我们通过将外观信息纳入模型来扩展该方法。为此，我们引入了一个基于点的表示图像数据相结合的位置和外观信息。然后，我们追求的概率对应的概念，并使用最大后验概率（MAP）的方法来推导出一个统计形状和外观模型。模型生成以及模型拟合可以表示为关于模型参数的单个全局优化准则。在第一次评估中，我们展示了所提出的方法的可行性，并使用2D肺部CT切片评估模型生成和基于模型的分割。
One-to-one correspondences are fundamental for the creation of classical statistical shape and appearance models. At the same time, the identification of these correspondences is the weak point of such model-based methods. Hufnagel et al.1 proposed an alternative method using correspondence probabilities instead of exact one-to- one correspondences for a statistical shape model. In this work, we extended the approach by incorporating appearance information into the model. For this purpose, we introduce a point-based representation of image data combining position and appearance information. Then, we pursue the concept of probabilistic correspondences and use a maximum a-posteriori (MAP) approach to derive a statistical shape and appearance model. The model generation as well as the model fitting can be expressed as a single global optimization criterion with respect to model parameters. In a first evaluation, we show the feasibility of the proposed approach and evaluate the model generation and model-based segmentation using 2D lung CT slices.