Journal of Heuristics

, Volume 18, Issue 1, pp 169–192 | Cite as

GRASP and path relinking hybridizations for the point matching-based image registration problem

  • José SantamaríaEmail author
  • Oscar Cordón
  • Sergio Damas
  • Rafael Martí
  • Ricardo J. Palma


In the last decade, image registration has proven to be a very active research area when tackling computer vision problems, especially in medical applications. In general, image registration methods aim to find a transformation between two images taken under different conditions. Point matching is an image registration approach based on searching for the right pairing of points between the two images, which involves a combinatorial optimization problem. From this matching, the registration transformation can be inferred by means of numerical methods.

In this paper, we tackle the medical image registration problem by means of a recent hybrid metaheuristic composed of two well-known optimization methods: GRASP and path relinking. Several designs based on this new hybrid approach have been tested. Our experimentation with real-world problems shows the combination of GRASP and evolutionary path relinking performs well when compared to previous state-of-the-art image registration approaches adopting both the point matching and transformation parameter approaches.


Metaheuristics GRASP Path relinking Scatter search Computer vision Medical image registration 


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  1. Andrade, D.V., Resende, M.G.C.: Grasp with evolutionary path-relinking. In: 7th Metaheuristics International Conference (MIC 2007) (2007) Google Scholar
  2. Arun, K.S., Huang, T.S., Blostein, S.D.: Least-squares fitting of two 3-D points sets. IEEE Trans. Pattern Anal. Mach. Intell. 9(5), 698–700 (1987) CrossRefGoogle Scholar
  3. Brown, L.G.: A survey of image registration techniques. ACM Comput. Surv. 24(4), 325–376 (1992) CrossRefGoogle Scholar
  4. Chow, C.K., Tsui, H.T., Lee, T.: Surface registration using a dynamic genetic algorithm. Pattern Recognit. 37, 105–117 (2004) CrossRefGoogle Scholar
  5. Cordón, O., Damas, S.: Image registration with iterated local search. J. Heuristics 12, 73–94 (2006) CrossRefGoogle Scholar
  6. Cordón, O., Damas, S., Santamaría, J.: Feature-based image registration by means of the CHC evolutionary algorithm. Image Vis. Comput. 22, 525–533 (2006) CrossRefGoogle Scholar
  7. Cordón, O., Damas, S., Santamaría, J., Martí, R.: Scatter search for the 3D point matching problem in image registration. INFORMS J. Comput. 20, 55–68 (2008) MathSciNetCrossRefGoogle Scholar
  8. Dasgupta, S., Banerjee, A.: Pattern tracking and 3-D motion reconstruction of a rigid body from a 2-D image sequence. IEEE Trans. Syst. Man Cybern. 35(1), 116–125 (2005) CrossRefGoogle Scholar
  9. de Falco, I., Della Cioppa, A., Maisto, D., Tarantino, E.: Differential evolution as a viable tool for satellite image registration. Appl. Soft Comput. 8(4), 1453–1462 (2008) CrossRefGoogle Scholar
  10. Faria, H., Binato, S., Resende, M.G.C., Falcao, D.J.: Transmission network design by a greedy randomized adaptive path relinking approach. IEEE Trans. Power Syst. 20, 43–49 (2005) CrossRefGoogle Scholar
  11. Feo, T.A., Resende, M.G.C.: A probabilistic heuristic for a computationally difficult set covering problem. Oper. Res. Lett. 8, 67–71 (1989) MathSciNetCrossRefGoogle Scholar
  12. Glover, F., Laguna, M.: Tabu Search. Kluwer Academic, Dordrecht (1997) CrossRefGoogle Scholar
  13. Goshtasby, A.: 2D and 3D Image Registration. Wiley Interscience, New York (2005) Google Scholar
  14. Horn, B.K.P.: Closed-form solution of absolute orientation using unit quaternions. J. Opt. Soc. Am. 4, 629–642 (1987) CrossRefGoogle Scholar
  15. Kim, J., Byun, S., Ahn, B.: Fast full search motion estimation algorithm using various matching scans in video coding. IEEE Trans. Syst. Man Cybern. 31(4), 540–548 (2001) CrossRefGoogle Scholar
  16. Kwan, R.K.S., Evans, A.C., Pike, G.B.: MRI simulation-based evaluation of image-processing and classification methods. IEEE Trans. Med. Imaging 18(11), 1085–1097 (1999) CrossRefGoogle Scholar
  17. Laguna, M., Martí, R.: GRASP and path relinking for 2-layer straight line crossing minimization. INFORMS J. Comput. 11(1), 44–52 (1999) CrossRefGoogle Scholar
  18. Liu, Y.: Improving ICP with easy implementation for free form surface matching. Pattern Recognit. 37(2), 211–226 (2004) CrossRefGoogle Scholar
  19. Lozano, M., García-Martínez, C.: Hybrid metaheuristics with evolutionary algorithms specializing in intensification and diversification: overview and progress report. Comput. Oper. Res. 37(3), 481–497 (2010) MathSciNetCrossRefGoogle Scholar
  20. Luck, J.P., Little, C.Q., Hoff, W.: Registration of range data using a hybrid simulated annealing and iterative closest point algorithm. In: IEEE International Conference on Robotics and Automation (ICRA’00), pp. 3739–3744 (2000) Google Scholar
  21. Marai, G.E., Laidlaw, D.H., Crisco, J.J.: Super-resolution registration using tissue-classified distance fields. IEEE Trans. Med. Imaging 25(2), 177–187 (2006) CrossRefGoogle Scholar
  22. Monga, O., Benayoun, S., Faugeras, O.: From partial derivatives of 3-D density images to ridges lines. In: Computer Vision and Pattern Recognition, IEEE, Champaign, Illinois, USA, pp. 354–389 (1992) Google Scholar
  23. Prais, M., Ribeiro, C.: Reactive GRASP: An application to a matrix decomposition problem in TDMA traffic assignment. INFORMS J. Comput. 12(3), 164–176 (2000) MathSciNetCrossRefGoogle Scholar
  24. Resende, M.G.C., Ribeiro, C.C.: Greedy randomized adaptive search procedures. In: Glover, F., Kochenberger, G. (eds.) Handbook of Metaheuristics, pp. 219–249. Kluwer Academic, Dordrecht (2003) Google Scholar
  25. Resende, M.G.C., Werneck, R.F.: A hybrid heuristic for the p-median problem. J. Heuristics 10, 59–88 (2004) CrossRefGoogle Scholar
  26. Resende, M.G.C., Martí, R., Gallego, M., Duarte, A.: Grasp and path relinking for the max-min diversity problem. Comput. Oper. Res. 37, 498–508 (2010) MathSciNetCrossRefGoogle Scholar
  27. Robertson, C., Fisher, R.B.: Parallel evolutionary registration of range data. Comput. Vis. Image Underst. 87, 39–50 (2002) CrossRefGoogle Scholar
  28. Silva, L., Bellon, O.R.P., Boyer, K.L.: Precision range image registration using a robust surface interpenetration measure and enhanced genetic algorithms. IEEE Trans. Pattern Anal. Mach. Intell. 27(5), 762–776 (2005) CrossRefGoogle Scholar
  29. Wachowiak, M.P., Smolikova, R., Zheng, Y., Zurada, J.M., El-Maghraby, A.S.: An approach to multimodal biomedical image registration utilizing particle swarm optimization. IEEE Trans. Evol. Comput. 8(3), 289–301 (2004) CrossRefGoogle Scholar
  30. Wang, F.: An efficient coordinate frame calibration method for 3-D measurement by multiple camera systems. IEEE Trans. Syst. Man Cybern. 35(3), 453–464 (2005) Google Scholar
  31. Yamany, S.M., Ahmed, M.N., Farag, A.A.: A new genetic-based technique for matching 3D curves and surfaces. Pattern Recognit. 32, 1817–1820 (1999) CrossRefGoogle Scholar
  32. Zitová, B., Flusser, J.: Image registration methods: a survey. Image Vis. Comput. 21, 977–1000 (2003) CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • José Santamaría
    • 1
    Email author
  • Oscar Cordón
    • 2
    • 3
  • Sergio Damas
    • 2
  • Rafael Martí
    • 4
  • Ricardo J. Palma
    • 5
  1. 1.Department of Computer Science, EPS de LinaresUniversity of JaénJaénSpain
  2. 2.European Centre for Soft Computing, Edif. Científico-TecnológicoMieresSpain
  3. 3.Department of Computer Science and Artificial Intelligence, E.T.S.I. Informática y TelecomunicaciónUniversity of GranadaGranadaSpain
  4. 4.Department of Statistics and Operations Research, Facultad de MatemáticasUniversity of ValenciaBurjassotSpain
  5. 5.Department of Computer Science and Artificial Intelligence, ETSIITUniversity of GranadaGranadaSpain

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