Abstract. Feature point tracking and detection of X-ray images is challenging due to overlapping anatomical structures of different depths, which lead to low-contrast images. Tracking of motion in X-ray sequences can support many clinical applications like motion compensation or 2D / 3D registration algorithms. This paper is the first to evaluate the performance of several feature tracking and detection algorithms on artificial and real X-ray image sequences, which involve rigid motion as well as external disturbances. A stand-alone application has been developed to provide an overall test bench for all algorithms, realized by OpenCV implementations. Experiments show that the Kanade-Lucas-Tomasi tracker is the most consistent and effective tracking algorithm. Considering external disturbances, template matching provides the most sufficient results. Furthermore, the influence of feature point detection methods on tracking results is shown.
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