Multimodality Image Registration by Particle Swarm Optimization of Mutual Information

• Published in: Advanced Intelligent Computing Theories and Applications. With Aspects of Artificial Intelligence pp. 1120 - 1130
• Main Authors: Li, Qi, Sato, Isao
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg
• Series: Lecture Notes in Computer Science
• Subjects: artificial intelligence; image registration; multimodality; mutual information; particle swarm optimization
• ISBN: 9783540742012; 3540742018
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-540-74205-0_116

The goal of image registration is to align two or more images of the same scene. To fully automate the registration process by optimization of the mutual information criterion, a robust global optimizer is indispensable. This paper focuses mainly on application and evaluation of particle swarm optimization for the rigid transformation registration of multimodality images. Four different modes of particle swarm optimization are proposed to globally optimize the challenging intensity based image registration. The functional manifest of different modes is comparatively evaluated by rigid experiments. The results show that the particle swarm optimization algorithm is to be promising for optimal multimodality image registration. With consideration of the trade-off between the successful rate and the computation time, the proposed PSO Mode II gives relatively best performance during the experimental studies of multimodality image registration.