SharpMean: groupwise registration guided by sharp mean image and tree-based registration.
SharpMean: groupwise registration guided by sharp mean image and tree-based registration.
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DOI：
10.1016/j.neuroimage.2011.03.050
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发表时间：
2011-06-15
期刊：
影响因子：
5.7
通讯作者：
Shen, Dinggang
中科院分区：
文献类型：
--
作者：
Wu, Guorong;Jia, Hongjun;Wang, Qian;Shen, Dinggang
分组登记由于其对人口数据的无偏见分析的吸引力而变得越来越受欢迎。用于分组配准的最流行的方法之一是迭代地计算组平均图像，然后朝向最新估计的组平均图像配准所有对象图像。然而，它的性能可能会被削弱的模糊平均图像估计在groupwise注册过程的最开始，因为所有的主题图像是远远没有很好地对齐在那一刻。在本文中，我们首先指出的意义，始终保持组平均图像清晰和清晰的整个groupwise注册过程中，这是直观的重要，但尚未在文献中探讨。为了实现这一点，我们诉诸于开发鲁棒的平均图像估计的自适应加权策略，其中的权重是自适应的，不仅在个别的主题图像，但也在图像域中的所有空间位置。另一方面，我们注意到，一些受试者可能有很大的解剖变化，从组平均图像，这挑战了大多数国家的最先进的配准算法。为了确保良好的配准结果，在每次迭代中，我们探索的主题图像的流形，并建立一个最小生成树（MST）与组平均图像作为根的MST。因此，每个对象图像仅被配准到其通常具有相似形状的父节点，并且其到组平均图像空间的整体变换是通过沿着将其自身连接到MST（组平均图像）的根的路径连接沿着的所有变形来获得的。因此，所有的主题将很好地对准组平均图像自适应。我们的方法已经在真实的和模拟数据集进行了评估。在所有的实验中，我们的方法优于传统的算法，一般产生一个模糊的组平均图像在整个groupwise注册。
Groupwise registration has become more and more popular due to its attractiveness for unbiased analysis of population data. One of the most popular approaches for groupwise registration is to iteratively calculate the group mean image and then register all subject images towards the latest estimated group mean image. However, its performance might be undermined by the fuzzy mean image estimated in the very beginning of groupwise registration procedure, because all subject images are far from being well-aligned at that moment. In this paper, we first point out the significance of always keeping the group mean image sharp and clear throughout the entire groupwise registration procedure, which is intuitively important but has not been explored in the literature yet. To achieve this, we resort to developing the robust mean-image estimator by the adaptive weighting strategy, where the weights are adaptive across not only the individual subject images but also all spatial locations in the image domain. On the other hand, we notice that some subjects might have large anatomical variations from the group mean image, which challenges most of the state-of-the-art registration algorithms. To ensure good registration results in each iteration, we explore the manifold of subject images and build a minimal spanning tree (MST) with the group mean image as the root of the MST. Therefore, each subject image is only registered to its parent node often with similar shapes, and its overall transformation to the group mean image space is obtained by concatenating all deformations along the paths connecting itself to the root of the MST (the group mean image). As a result, all the subjects will be well aligned to the group mean image adaptively. Our method has been evaluated in both real and simulated datasets. In all experiments, our method outperforms the conventional algorithm which generally produces a fuzzy group mean image throughout the entire groupwise registration.
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影响因子：
4.8
作者：
Wang, Qian;Chen, Liya;Yap, Pew-Thian;Wu, Guorong;Shen, Dinggang
通讯作者：
Shen, Dinggang
影响因子：
10.9
作者：
Maintz, J B;Viergever, M A
通讯作者：
Viergever, M A
影响因子：
5.7
作者：
Shattuck, David W.;Mirza, Mubeena;Toga, Arthur W.
通讯作者：
Toga, Arthur W.
DOI：
10.1023/b:visi.0000043755.93987.aa
发表时间：
2005-02-01
影响因子：
19.5
作者：
Beg, MF;Miller, MI;Younes, L
通讯作者：
Younes, L
影响因子：
5.7
作者：
Tang, Songyuan;Fan, Yong;Shen, Dinggang
通讯作者：
Shen, Dinggang