Maximization of feature potential mutual information in multimodality image registration using particle swarm optimization

Abstract

Standard Mutual Information function contains local maxima, which make against to convergence of registration transformation parameters for automated multimodality image registration problems. We proposed Feature Potential Mutual Information (FPMI) to increases the smoothness of the registration measure function and use Particle Swarm Optimization to search the optimal registration transformation parameter in this paper. At first, Edges of images are detected. Next, edge feature potential is defined by expanding edges to the neighborhood region using potential function. Each edge point influences the whole potential field, just like the particle of physics in the gravitation field space. FPMI is computed on the edge feature potential of two images. It substitutes the edge feature potential values for gray values in images. It can avoid great change of joint probability distribution and has less local maxima. The registration accuracy of FPMI is analyzed under different edge detection cases. It is shown that the registration accuracy of FPMI is more accurate and more robust than that of MI. Maximization of FPMI is done by PSO. PSO combines local search methods with global search methods, attempting to balance exploration and exploitation. Its complex behavior follows from a few simple rules and has less computational complexity. Multimodal medical images are used to compare the response of FPMI and MI to translation and rotation. Experiments show that FPMI is smoother and has less local fluctuations than that of MI. Registration results show that PSO do it better than Powell’s method to search the optimal registration parameters.