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Metaheuristics for Medical Image Registration

In: Handbook of Heuristics

Author

Listed:
  • 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)

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.

Suggested Citation

  • Andrea Valsecchi & Enrique Bermejo & Sergio Damas & Oscar Cordón, 2018. "Metaheuristics for Medical Image Registration," Springer Books, in: Rafael Martí & Panos M. Pardalos & Mauricio G. C. Resende (ed.), Handbook of Heuristics, chapter 36, pages 1079-1101, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-07124-4_56
    DOI: 10.1007/978-3-319-07124-4_56
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