Amit Y
Department of Statistics, University of Chicago, IL 60637, USA.
IEEE Trans Med Imaging. 1997 Feb;16(1):28-40. doi: 10.1109/42.552053.
A new method of model registration is proposed using graphical templates. A decomposable graph of landmarks is chosen in the template image. All possible candidates for these landmarks are found in the data image using robust relational local operators. A dynamic programming algorithm on the template graph finds the optimal match to a subset of the candidate points in polynomial time. This combination--local operators to describe points of interest/landmarks and a graph to describe their geometric arrangement in the plane--yields fast and precise matches of the model to the data with no initialization required. In addition, it provides a generic tool box for modeling shape in a variety of applications. This methodology is applied in the context of T2-weighted magnetic resonance (MR) axial and sagittal images of the brain to identify specific anatomies.
提出了一种使用图形模板进行模型配准的新方法。在模板图像中选择一个可分解的地标图。使用鲁棒的关系局部算子在数据图像中找到这些地标的所有可能候选点。模板图上的动态规划算法在多项式时间内找到与候选点子集的最佳匹配。这种结合——用局部算子描述感兴趣点/地标，用图描述它们在平面中的几何排列——无需初始化就能快速精确地将模型与数据进行匹配。此外，它为各种应用中的形状建模提供了一个通用工具箱。该方法应用于大脑的T2加权磁共振（MR）轴向和矢状图像，以识别特定的解剖结构。
