Robust Optimization Using Disturbance for Image Registration

This paper exploits the different properties between the local neighborhood of global optimum and those of local optima in image registration optimization. Namely, a global optimum has a larger capture neighborhood, in which from any location a monotonic path exists to reach this optimum, than any other local optima. With these properties, we propose a simple and computationally efficient technique using transformation disturbance to assist an optimization algorithm to avoid local optima, and hence to achieve a robust optimization. We demonstrate our method on 3D rigid registrations by using mutual information as similarity measure, and we adopt quaternions to represent rotations for the purpose of the unique and order-independent expression. Randomized registration experiments on four clinical CT and MR-T1 datasets show that the proposed method consistently gives much higher success rates than the conventional multi-resolution mutual information based method. The accuracy of our method is also high.