Yang Jing, Staib Lawrence H, Duncan James S
Departments of Electrical Engineering, Yale University, New Haven, CT 06520-8042, USA.
Inf Process Med Imaging. 2003 Jul;18:198-209. doi: 10.1007/978-3-540-45087-0_17.
A novel method for the segmentation of multiple objects from 3D medical images using inter-object constraints is presented. Our method is motivated by the observation that neighboring structures have consistent locations and shapes that provide configurations and context that aid in segmentation. We define a Maximum A Posteriori(MAP) estimation framework using the constraining information provided by neighboring objects to segment several objects simultaneously. We introduce a representation for the joint density function of the neighbor objects, and define joint probability distributions over the variations of the neighboring positions and shapes of a set of training images. By estimating the MAP shapes of the objects, we formulate the model in terms of level set functions, and compute the associated Euler-Lagrange equations. The contours evolve both according to the neighbor prior information and the image gray level information. We feel that this method is useful in situations where there is limited inter-object information as opposed to robust global atlases. Results and validation from various experiments on synthetic data and medical imagery in 2D and 3D are demonstrated.
提出了一种利用对象间约束从3D医学图像中分割多个对象的新方法。我们的方法基于这样的观察：相邻结构具有一致的位置和形状，这些位置和形状提供了有助于分割的配置和上下文信息。我们使用相邻对象提供的约束信息定义了一个最大后验（MAP）估计框架，以同时分割多个对象。我们引入了一种相邻对象联合密度函数的表示方法，并定义了一组训练图像相邻位置和形状变化的联合概率分布。通过估计对象的MAP形状，我们根据水平集函数来构建模型，并计算相关的欧拉-拉格朗日方程。轮廓根据相邻先验信息和图像灰度信息进行演化。我们认为，与强大的全局图谱相比，这种方法在对象间信息有限的情况下很有用。展示了在2D和3D合成数据及医学图像上进行的各种实验的结果和验证。
