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Constructing Data-Driven Optimal Representations for Iterative Pairwise Non-rigid Registration

机译:构建数据驱动的最佳表示,用于迭代成对非刚性注册

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Non-rigid registration of a pair of images depends on the generation of a dense deformation field across one of the images. Such deformation fields can be represented by the deformation of a set of knotpoints, interpolated to produce the continuous deformation field. This paper addresses the question of how best to choose the knotpoints of such a representation based on all of the available image information. These knotpoints are not landmarks, they can be positioned anywhere in the images, and do not necessarily correspond to any image feature. We use an iterative, data-driven algorithm for the selection of knotpoints, and a novel spline that interpolates smoothly between knotpoints. The algorithm produces a low-dimensional representation of the deformation field that can be successively refined in a multi-resolution manner. We demonstrate the properties of the algorithm on sets of 2D images and discuss the extension of the algorithm to 3D data.
机译:一对图像的非刚性配准取决于跨图像中的​​一个致密变形字段的产生。这种变形场可以由一组knotpoints的变形来表示,内插以产生连续变形场。本文解决了基于所有可用图像信息选择如何最好地选择这种表示的knotpoints的问题。这些knotpoints不是地标,它们可以定位在图像中的任何位置,并且不一定对应于任何图像特征。我们使用迭代,数据驱动算法来选择knotpoints,以及在Knotpoints之间平稳地插值的新型样条。该算法产生可以以多分辨率方式连续地改进的变形字段的低维表示。我们展示了在2D图像组上的算法的特性,并讨论算法到3D数据的扩展。

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