Mono-modal Medical Image Registration with Coral Reef Optimization

  • E. BermejoEmail author
  • M. Chica
  • S. Damas
  • S. Salcedo-Sanz
  • O. Cordón
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10870)


Image registration (IR) involves the transformation of different sets of image data having a shared content into a common coordinate system. To achieve this goal, the search for the optimal correspondence is usually treated as an optimization problem. The limitations of traditional IR methods have boomed the application of metaheuristic-based approaches to solve the problem while improving the performance. In this contribution, we consider a recent bio-inspired method: the Coral Reef Optimization Algorithm (CRO). This novel algorithm simulates the natural phenomena underlying a coral reef. We adapt the algorithm following two different approaches: feature-based and intensity-based designs and perform a thorough experimental study in a medical IR problem considering similarity transformations. The results show that CRO overcome the state-of-the-art results in terms of robustness, accuracy, and efficiency considering both approaches.


Coral Reefs Optimization Intensity-based Approach Median Squared Error (MedSE) Feature-based Methods Restart Mechanism 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work has been partially supported by the projects TIN2015-67661-P, including European Regional Development Funds (ERDF), from the Spanish Ministery of Economy, and TIN2014-54583-C2-2-R from the Spanish Ministerial Commission of Science and Technology (MICYT).


  1. 1.
    Bermejo, E., Chica, M., Salcedo-Sanz, S., Cordon, O.: Coral reef optimization for intensity-based medical image registration. In: IEEE Congress on Evolutionary Computation, CEC 2017, Proceedings, pp. 533–540. IEEE (2017)Google Scholar
  2. 2.
    Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. 14(2), 239–256 (1992)CrossRefGoogle Scholar
  3. 3.
    Collins, D.L., Zijdenbos, A.P., Kollkian, V., Sled, J.G., Kabani, N.J., Holmes, C.J., Evans, A.C.: Design and construction of a realistic digital brain phantom. IEEE Trans. Med. Imaging 17, 463–468 (1998)CrossRefGoogle Scholar
  4. 4.
    Damas, S., Cordón, O., Santamaría, J.: Medical image registration using evolutionary computation: an experimental survey. IEEE Comput. Intell. Mag. 6(4), 26–42 (2011)CrossRefGoogle Scholar
  5. 5.
    Goshtasby, A.A.: 2-D and 3-D Image Registration. Wiley Interscience, Hoboken (2005)Google Scholar
  6. 6.
    He, R., Narayana, P.A.: Global optimization of mutual information: application to three-dimensional retrospective registration of magnetic resonance images. Comput. Med. Imaging Graph. 26(4), 277–292 (2002)CrossRefGoogle Scholar
  7. 7.
    Klein, S., Staring, M., Murphy, K., Viergever, M.A., Pluim, J.P.W.: Elastix: a toolbox for intensity-based medical image registration. IEEE Trans. Med. Imaging 29(1), 196–205 (2010)CrossRefGoogle Scholar
  8. 8.
    Lozano, M., Herrera, F., Krasnogor, N., Molina, D.: Real-coded memetic algorithms with crossover hill-climbing. Evolut. Comput. 12(3), 273–302 (2004)CrossRefGoogle Scholar
  9. 9.
    Monga, O., Benayoun, S., Faugeras, O.: From partial derivatives of 3-D density images to ridge lines. In: Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1808, pp. 354–359. IEEE, Champaign (1992)Google Scholar
  10. 10.
    Salcedo-Sanz, S.: A review on the coral reefs optimization algorithm: new development lines and current applications. Prog. Artif. Intell. 6(1), 1–15 (2017)CrossRefGoogle Scholar
  11. 11.
    Salcedo-Sanz, S., Del Ser, J., Landa-Torres, I., Gil-López, S., Portilla-Figueras, J.A.: The coral reefs optimization algorithm: a novel metaheuristic for efficiently solving optimization problems. Sci. World J. 2014, 1–15 (2014)Google Scholar
  12. 12.
    Santamaría, J., Cordón, O., Damas, S., García-Torres, J., Quirin, A.: Performance evaluation of memetic approaches in 3D reconstruction of forensic objects. Soft Comput. 13(8–9), 883–904 (2009)CrossRefGoogle Scholar
  13. 13.
    Takahashi, M., Kita, H.: A crossover operator using independent component analysis for real-coded genetic algorithms. In: Proceedings of the 2001 Congress on Evolutionary Computation (IEEE Cat. No.01TH8546), vol. 1, pp. 643–649 (2001)Google Scholar
  14. 14.
    Valsecchi, A., Damas, S., Santamaria, J., Marrakchi-Kacem, L.: Genetic algorithms for voxel-based medical image registration. In: 2013 Fourth International Workshop on Computational Intelligence in Medical Imaging (CIMI), pp. 22–29, Aprril 2013Google Scholar
  15. 15.
    Valsecchi, A., Damas, S., Santamaría, J., Marrakchi-Kacem, L.: Intensity-based image registration using scatter search. Artif. Intell. Med. 60(3), 151–163 (2014)CrossRefGoogle Scholar
  16. 16.
    Zitová, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21(11), 977–1000 (2003)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • E. Bermejo
    • 1
    Email author
  • M. Chica
    • 2
  • S. Damas
    • 3
  • S. Salcedo-Sanz
    • 4
  • O. Cordón
    • 1
    • 5
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of GranadaGranadaSpain
  2. 2.School of Electrical Engineering and ComputingThe University of NewcastleCallaghanAustralia
  3. 3.Department of Software EngineeringUniversity of GranadaGranadaSpain
  4. 4.Department of Signal Theory and CommunicationsUniversity of AlcaláAlcalá de HenaresSpain
  5. 5.Research Center on Information and Communication TechnologiesUniversity of GranadaGranadaSpain

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