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2018 | OriginalPaper | Buchkapitel

36. Metaheuristics for Medical Image Registration

verfasst von: Andrea Valsecchi, Enrique Bermejo, Sergio Damas, Oscar Cordón

Erschienen in: Handbook of Heuristics

Verlag: Springer International Publishing

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Abstract

In the last few decades, image registration (IR) has been a very active research area in computer vision. Applications of IR cover a broad range of real-world problems, including remote sensing, medical imaging, artificial vision, and computer-aided design. In particular, medical IR is a mature research field with theoretical support and two decades of practical experience. Formulated as either a continuous or combinatorial optimization problem, medical IR has been traditionally tackled by iterative numerical optimization methods, which are likely to get stuck in local optima and deliver suboptimal solutions. Recently, a large number of medical IR methods based on different metaheuristics, mostly belonging to evolutionary computation, have been proposed. In this chapter, we review the most recognized of these algorithms and develop an experimental comparison over real-world IR scenarios.
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Metadaten
Titel
Metaheuristics for Medical Image Registration
verfasst von
Andrea Valsecchi
Enrique Bermejo
Sergio Damas
Oscar Cordón
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-07124-4_56

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