Pock Thomas, Urschler Martin, Zach Christopher, Beichel Reinhard, Bischof Horst
Institute for Computer Graphics & Vision, Graz University of Technology, Austria.
Med Image Comput Comput Assist Interv. 2007;10(Pt 2):511-8. doi: 10.1007/978-3-540-75759-7_62.
Nonlinear image registration is a challenging task in the field of medical image analysis. In many applications discontinuities may be present in the displacement field, and intensity variations may occur. In this work we therefore utilize an energy functional which is based on Total Variation regularization and a robust data term. We propose a novel, fast and stable numerical scheme to find the minimizer of this energy. Our approach combines a fixed-point procedure derived from duality principles combined with a fast thresholding step. We show experimental results on synthetic and clinical CT lung data sets at different breathing states as well as registration results on inter-subject brain MRIs.
非线性图像配准是医学图像分析领域一项具有挑战性的任务。在许多应用中，位移场可能存在不连续性，并且可能会出现强度变化。因此，在这项工作中，我们使用了一种基于总变差正则化和稳健数据项的能量泛函。我们提出了一种新颖、快速且稳定的数值方案来找到该能量的最小值。我们的方法将源自对偶原理的不动点过程与快速阈值化步骤相结合。我们展示了在不同呼吸状态下的合成和临床CT肺部数据集上的实验结果以及受试者间脑MRI的配准结果。
