RANSAC-based ENT algorithm for robust detection and segmentation of cylindrical fragments from calibrated C-arm images

Automated identification, pose and size estimation of cylindrical fragments from registered C-arm images is highly desirable in various computer-assisted, fluoroscopy-based applications including long bone fracture reduction and intramedullary nailing, where the pose and size of bone fragment need to be accurately estimated for a better treatment. In this paper, a RANSAC-based EM algorithm for robust detection and segmentation of cylindrical fragments from calibrated C-arm images is presented. By detection, we mean that the axes and the radii of the principal fragments will be automatically determined. And by segmentation, we mean that the contour of the fragment projection onto each image plane will be automatically extracted. Benefited from the cylindrical shape of the fragments, we formulate the detection problem as an optimal process for fitting parameterized three-dimensional (3D) cylinder model to images. A RANSAC-based EM algorithm is proposed to find the optimal solution by converting the fragment detection procedure to an iterative closest point (ICP) matching procedure. The outer projection boundary of the estimated cylinder model is then fed to a region-based active contour model to robustly extract the contour of the fragment projection. The proposed algorithm has been successfully applied to real patient data with/without external objects, yielding promising results.