Zöllei Lilla, Fisher John W, Wells William M
Massachusetts Institute of Technology, Artificial Intelligence Laboratory, Cambridge, MA 02139, USA.
Inf Process Med Imaging. 2003 Jul;18:366-77. doi: 10.1007/978-3-540-45087-0_31.
We formulate and interpret several registration methods in the context of a unified statistical and information theoretic framework. A unified interpretation clarifies the implicit assumptions of each method yielding a better understanding of their relative strengths and weaknesses. Additionally, we discuss a generative statistical model from which we derive a novel analysis tool, the auto-information function, as a means of assessing and exploiting the common spatial dependencies inherent in multi-modal imagery. We analytically derive useful properties of the auto-information as well as verify them empirically on multi-modal imagery. Among the useful aspects of the auto-information function is that it can be computed from imaging modalities independently and it allows one to decompose the search space of registration problems.
我们在统一的统计和信息理论框架内制定并解释了几种配准方法。统一的解释阐明了每种方法的隐含假设，从而能更好地理解它们的相对优缺点。此外，我们讨论了一种生成式统计模型，从中推导出一种新颖的分析工具——自信息函数，作为评估和利用多模态图像中固有公共空间依赖性的一种手段。我们通过分析得出了自信息的有用属性，并在多模态图像上进行了实证验证。自信息函数的有用之处在于它可以独立地从成像模态中计算出来，并且它允许人们分解配准问题的搜索空间。
