Feature-based piecewise rigid registration in 2-D medical images

Piecewise rigid registration is a fundamental problem in medical imaging involving intra-patient pose differences in multiple medical images, mostly due to articulated motion. In this paper, we propose a method to extract multiple rigid transformations in 2D medical images in the presence of outliers. First, points of interest in the images are extracted and matched with the SIFT algorithm. Secondly, multiple rigid motions are sampled and clustered by the mean shift algorithm in the special Euclidean group SE(2), a smooth manifold of 2-D rigid transformation matrices. The method proposed is evaluated for intra-subject registrations of knee fluoroscopy images, demonstrating a mean angular and translational error on the estimated motion of 0.39° and 6.65 pixels, respectively.