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An Inhomogeneous Multi-resolution Regularization Concept for Discontinuity Preserving Image Registration

机译:不连续性不连续的多分辨率正则化概念,用于保存图像的不连续性

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Sliding organs pose challenges in the registration of dynamic medical images because the smoothness criterion which is commonly assumed over the whole image domain does not apply at the sliding interfaces. In this case, image registration methods have to cope with local discontinuities in the correspondence map. We present a new registration methodology based on a multi-resolution transformation model which is defined as a directed acyclic graph. The graph's edges connect consecutive resolution levels enabling to inhomogeneously pass displacements through to higher levels. Thus, they are well suited to cope with local discontinuities while aiming at smooth correspondence maps. We introduce three regularization terms which operate on the graph. A total variation term ensuring discontinuity preserving smoothness, a sparsity term on zero edge-weights to prevent trivial solutions and a term which prefers transformations which are explained in lower resolution levels. For an early proof of concept we analyze the registration performance of our method on synthetic 2D data and on a 2D slice of the POPI model.
机译:滑动器官对动态医学图像的配准提出了挑战,因为通常在整个图像域中假定的平滑度标准不适用于滑动界面。在这种情况下,图像配准方法必须应对对应图中的局部不连续性。我们提出了一种基于多分辨率转换模型的新注册方法,该模型被定义为有向无环图。图形的边缘连接了连续的分辨率级别,从而可以将位移不均匀地传递到更高的级别。因此,它们非常适合处理局部不连续性,同时目标是平滑的对应图。我们介绍在图形上运行的三个正则化项。确保不连续性,保持平滑度的总变化项,零边缘权重的稀疏性项(防止微不足道的解决方案)和更喜欢以较低分辨率级别说明的变换的项。作为概念的早期证明,我们分析了我们的方法在合成2D数据和POPI模型的2D切片上的配准性能。

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