A Hybrid Particle Swarm Steepest Gradient Algorithm for Elastic Brain Image Registration

Abstract

Over the course of a neurosurgical procedure, the brain changes its shape in reaction to mechanical and physiological changes associated with the surgery. Hence the use of elastic registration is required. In this paper, we propose a hybrid particle swarm with gradient descent algorithm named as HPSO to solve the problem of Elastic brain Image Registration. The main idea is to find the best transformation function that aligns two images by maximizing a similarity measure through HPSO. There are two major optimization methods, global and local methods. The basic problem with local methods such as steepest gradient is that they usually trap in a local minimum. However, steepest gradient will usually converge even for poor initial approximation. On the other hand, the basic PSO as a global method is sensitive to its initial values. So, we decide to use the steepest gradient as a starting approximation for the PSO method. The results from our experiments show that this hybrid algorithm, besides its simplicity, provides a robust, accurate and effective way for elastic brain image registration.