Metaheuristics for Medical Image Registration

Andrea Valsecchi (), Enrique Bermejo (), Sergio Damas () and Oscar Cordón ()
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Andrea Valsecchi: Unviersity of Granada, Department of Computer Science and Artificial Intelligence
Enrique Bermejo: Unviersity of Granada, Department of Computer Science and Artificial Intelligence
Sergio Damas: University of Granada, Department Software Engineering
Oscar Cordón: Unviersity of Granada, Department of Computer Science and Artificial Intelligence

Chapter 36 in Handbook of Heuristics, 2018, pp 1079-1101 from Springer

Abstract: 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.

Keywords: Medical imaging; Image registration; Image segmentation (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-319-07124-4_56

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DOI: 10.1007/978-3-319-07124-4_56

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