A Distance Oriented Kalman Filter Particle Swarm Optimizer Applied to Multi-Modality Image Registration

TL;DR

This paper introduces a novel particle swarm optimizer enhanced with an unscented Kalman filter, significantly improving performance in multi-modality image registration tasks, especially with motion artifacts, compared to traditional methods.

Contribution

The paper presents a new PSO variant incorporating an unscented Kalman filter, improving convergence speed and accuracy in complex image registration scenarios.

Findings

Outperformed original PSO on 4 of 8 benchmark functions

Reduced registration errors by at least 2mm in brain images

Successfully registered images with motion artifacts that other methods failed

Abstract

In this paper we describe improvements to the particle swarm optimizer (PSO) made by inclusion of an unscented Kalman filter to guide particle motion. We demonstrate the effectiveness of the unscented Kalman filter PSO by comparing it with the original PSO algorithm and its variants designed to improve 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. Our methods also demonstrated an ability to align images featuring motion related artefacts 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.