High accuracy optical flow for 3D medical image registration using the census cost function

Abstract

In 2004, Brox et al. described how to minimize an energy functional for dense 2D optical flow estimation that enforces both intensity and gradient constancy. This paper presents a novel variant of their method, in which the census cost function is utilized in the data term instead of absolute intensity differences. The algorithm is applied to the task of pulmonary motion estimation in 3D computed tomography (CT) image sequences. The performance evaluation is based on DIR-lab benchmark data for lung CT registration. Results show that the presented algorithm can compete with current state-of-the-art methods in regards to both registration accuracy and run-time. © 2014 Springer-Verlag Berlin Heidelberg.