Distance Oriented Particle Swarm Optimizer for Brain image Registration

In this paper, we describe improvements to the particle swarm optimizer (PSO) made by the inclusion of an unscented Kalman filter to guide particle motion. We show how this method increases the speed of convergence, and reduces the likelihood of premature convergence, increasing the overall accuracy of optimization. We demonstrate the effectiveness of the unscented Kalman filter PSO by comparing it with the original PSO algorithm and its variants designed to improve the performance. The PSOs were tested firstly on a number of common synthetic benchmarking functions and secondly applied to a practical three-dimensional image registration problem. The proposed methods displayed better performances for 4 out of 8 benchmark functions and reduced the target registration errors by at least 2mm when registering down-sampled benchmark brain images. They also demonstrated an ability to align images featuring motion-related artifacts which all other methods failed to register. These new PSO methods provide a novel, efficient mechanism to integrate prior knowledge into each iteration of the optimization process, which can enhance the accuracy and speed of convergence in the application of medical image registration.