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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