基于图像集拓扑中心的群体配准方法

传统的图像配准通常指定一幅参考图像在配准过程中保持不变，将另一幅图像变换到参考图像空间，使得两幅图像在空间上互相匹配，从而可以精确比较两者之间的差异.针对多幅个体差异较大的图像配准问题，如果指定一幅作为参考，将其他图像配准到参考图像空间，则会引入该幅参考图像的个体形状偏差，从而影响最终的对比结果.为此，本文首先介绍了目前针对该问题的主要解决方法，然后提出了基于图像集拓扑中心的群体配准方法——TopologyCenter.为验证所提出的群体配准方法的性能，通过使用国外公开的数据集，详细比较了本文提出的方法与当前两种主要方法的群体配准结果的差异.实验结果表明，本文提出的方法具有更小的群体配准偏差，群体配准结果更好；同时，在对实验结果的评价中，本文还提出了一种简捷的群体偏差度量指标.

A Groupwise Registration Method Based on Topology Center of Images

Image registration is often used to transform images from different subjects into the same spatial space to ensure more accurate comparisons. With the traditional image registration methods, one image is usually specified as the reference image, while the other images are transformed into the space of the reference image. However, the image randomly selected as the reference image may have significant deviations from the group mean in datasets with large individual differences, causing registration biases and affecting the final results of groupwise comparisons. In this paper, a groupwise registration method based on the topology center of the image set was proposed. With the same open datasets, the performance of the proposed method was compared to that of two widely used traditional methods. The experimental results demonstrated that the proposed method had smaller groupwise registration bias and better registration results. The paper also proposed a simple measure for evaluating groupwise registration bias.