Acceleration and evaluation of block-based motion estimation algorithms for x-ray fluoroscopy

Abstract

This paper discusses acceleration methods for block-based motion estimation with the focus on the application to moving low-dose x-ray images (x-ray fluoroscopy). These images often exhibit a very low signal-to-noise ratio. If the frame rate is sufficiently high, these degradations can at least be partly compensated by temporally motion- compensated filtering, which requires first a motion estimation step. Due to the low signal-to-noise level and strong local motions standard algorithms are not suitable for application to fluoroscopy sequences. In an earlier work we developed a full search Bayesian block matching algorithm using spatial and temporal regularization. Based on the so-called Successive Elimination Algorithm by Li and Salari we developed several acceleration methods for the motion estimation step. Here, these methods and a detailed evaluation based on several synthetically generated motion types in fluoroscopy sequences are provided. Using a weighted block-norm based inequality, combined with an efficient calculation of the error measure which is partitioned into local measures, the number of search positions is remarkably reduced without significant loss in estimation quality.