基于互信息的医学图像配准实验

基于互信息的配准方法，包括互信息和归一化互信息方法，是目前医学图像配准中无创、自动且精度很高的一种方法。已经被广泛应用。但是在其目标函数中存在着一定程度的幅值振荡现象。特别是在单模态图像配准中。我们研究发现，产生这种振荡的原因除了插值赝像外，还有由配准过程中图像重叠部分发生变化而引起的熵的变化不确定性。由插值赝像所带来的振荡基本上可以被消除掉；由熵的变化不确定性所带来的振荡很难被消除，但是这种振荡作用在归一化互信息中影响不大。归一化互信息比互信息具有更高的稳健性，适合于更广的应用范围。

Implementation of Mutual Information Based Medical Image Registration Methods

Image registration methods based on mutual information, including mutual information and normalized mutual information, have been accepted as the most accurate and efficient methods. But there are many fluctuations in the registration functions that hinder the optimization procedure and lead to registration failure in intramodal registration. We found that besides the interpolation artifacts, the uncertainty of the changing of entropy with the changing of overlap also contributes to the fluctuations. The effect of interpolation artifacts can be eliminated, but it is difficult to eliminate the effect of uncertainty of entropy. Luckily, this effect is not significant in normalized mutual information. Normalized mutual information is more stable and robust than standard mutual information and its better performance and wider application can be expected.