Affiliation: European Centre for Soft Computing, Mieres, Spain.
Metaheuristics are techniques that use approximate and intuitive strategies to quickly find near-optimal solutions of complex optimization problems. A number of outstanding examples belong to evolutionary computation and swarm intelligence, two classes of methods inspired to biological phenomena. These techniques have been extensively and successfully applied to feature-based image registration in medicine. However, with the increase in computational power during the last decade, intensity-based (or voxel-based) image registration methods have been preferred in many medical imaging applications, due to their robustness, accuracy and applicability, in cases where landmarks or other features are not available or easy to detect. While traditional numerical optimization techniques are employed to solve the registration problem, a number of contributions in the literature support the use of metaheuristics to overcome the shortcomings of classic methods. The aim of the paper is to review the state of the art in the application of evolutionary computation and other metaheuristics to intensity-based medical image registration. The study considers both well-known techniques with a large number of references in the literature as well as recent, outstanding proposals. The analysis focuses on the design of the methods to highlight common and successful practices. In addition, recommendations and open research lines in the field are provided.