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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.

Craniofacial superimposition (CFS) is a skeleton-based technique that aims to provide identity to a skull through its superimposition with one or more photographs of candidate missing people. While traditionally performed by forensic experts, computer-aided CFS methods can now provide substantial speedups and are quickly progressing towards a large degree of automation. A current major limitation concerns the position of the mandible, which is required to be manually set by the expert beforehand in order to reproduce the facial expression of the subject in each available photograph. This is time-consuming and prone to errors. In this work, we address this issue by extending the state-of-the-art genetic algorithm-based method with the ability to allocate the mandible in the right position according to an anatomical model. Based on a dataset of simulated ante-mortem images with different mandible apertures and facial poses, we prove experimentally that the proposed method is able to effectively tackle cases displaying a much larger range of mandible positions. In fact, thanks to the new genetic design, it is able to outperform the original method, even when the mandible aperture is very small. [ABSTRACT FROM AUTHOR]