Markov Random Field-Based Layer Separation for Simulated X-Ray Image Sequences

Motion estimation in X-ray images is a challenging task due to transparently overlapping structures from different depths. We propose to separate an X-ray sequence into a static and a dynamic layer to facilitate motion estimation. The method exploits the idea to use the minimum intensity over time and a spatial smoothness prior for both layers. For numerical optimization, we propose a conditional Markov random field. In experiments on synthetic data, we achieve a root mean squared intensity difference of 36.7± 8.4 to the ground truth static layer. In addition, we show qualitative results that demonstrate an improved layer separation compared to state-of-the-art algorithms.