关键词: 全局最优, 图像配准, 局部最优, 粒子群算法

Abstract: biomedical image registration, or geometric alignment of two-dimensional and/or three-dimensional (3-D) image data, is becoming increasingly important in diagnosis, treatment planning, functional studies, computer-guided therapies, and in biomedical research. Registration based on intensity values usually requires optimization of some similarity function between the images. Local optimization techniques frequently fail because these functions with respect to transformation parameters are generally no convex and irregular and, therefore, global methods are often required. In this paper, a new evolutionary approach, particle swarm optimization, is adapted for biomedical image registration. Multimodal registrations with initial orientations far from the ground truth were performed on three volumes from different modalities. Results of optimizing the normalized mutual information similarity function were compared with various evolutionary strategies. The hybrid particle swarm technique produced more accurate registrations in many cases, with comparable convergence. These results demonstrate that particle swarm approaches, along with evolutionary techniques and local methods, are useful in image registration, and emphasize the need for hybrid approaches for difficult registration problems.

Key words: global optimization, image registration, local optimization, particle swarm optimization.