Abstract. We propose combining a supervoxel-based image representation with the concept of graph cuts as an efficient optimization technique for three-dimensional (3-D) deformable image registration. Due to the pixels/voxels-wise graph construction, the use of graph cuts in this context has been mainly limited to two-dimensional (2-D) applications. However, our work overcomes some of the previous limitations by posing the problem on a graph created by adjacent supervoxels, where the number of nodes in the graph is reduced from the number of voxels to the number of supervoxels. We demonstrate how a supervoxel image representation combined with graph cuts-based optimization can be applied to 3-D data. We further show that the application of a relaxed graph representation of the image, followed by guided image filtering over the estimated deformation field, allows us to model “sliding motion.” Applying this method to lung image registration results in highly accurate image registration and anatomically plausible estimations of the deformations. Evaluation of our method on a publicly available computed tomography lung image dataset leads to the observation that our approach compares very favorably with state of the art methods in continuous and discrete image registration, achieving target registration error of 1.16 mm on average per landmark.

中文翻译：

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使用引导图像过滤进行基于图割的可变形图像配准的超级体素


摘要。我们建议将基于超体素的图像表示与图割的概念相结合，作为三维（3-D）可变形图像配准的有效优化技术。由于像素/体素方式的图构造，在这种情况下图割的使用主要限于二维（2-D）应用。然而，我们的工作通过在由相邻超体素创建的图上提出问题来克服之前的一些限制，其中图中的节点数量从体素的数量减少到超体素的数量。我们演示了如何将超级体素图像表示与基于图切割的优化相结合应用于 3D 数据。我们进一步表明，应用图像的松弛图形表示，然后对估计的变形场进行引导图像过滤，使我们能够对“滑动运动”进行建模。将此方法应用于肺部图像配准可实现高精度的图像配准和解剖学上合理的变形估计。在公开可用的计算机断层扫描肺部图像数据集上对我们的方法进行评估后发现，我们的方法在连续和离散图像配准方面与最先进的方法相比非常有利，每个地标平均实现了 1.16 毫米的目标配准误差。