In the last few decades, image registration (IR) has been established as a very active research area in computer vision. Over the years, it has been applied to a broad range of real-world problems ranging from remote sensing to medical imaging, artificial vision, and computer-aided design. IR has been usually tackled by iterative approaches considering numerical optimization methods which are likely to get stuck in local optima. Recently, a large number of IR methods based on the use of metaheuristics and evolutionary computation paradigms has been proposed providing outstanding results. In this contribution, we aim to develop a preliminary experimental study on some of the most recognized feature-based IR methods considering evolutionary algorithms. To do so, the IR framework is first presented and a brief description of some prominent evolutionary-based IR proposals are reviewed. Finally, a selection of some of the most representative methods are benchmarked facing challenging 3D medical image registration problem instances.
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