Advertisement

A Comparative Study of Bio-inspired Algorithms for Medical Image Registration

  • D. R. Sarvamangala
  • Raghavendra V. Kulkarni
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 687)

Abstract

The challenge of determining optimal transformation parameters for image registration has been treated traditionally as a multidimensional optimization problem. Non-rigid registration of medical images has been approached in this article using the particle swarm optimization algorithm, dragonfly algorithm, and the artificial bee colony algorithm. Brief introductions to these algorithms have been presented. Results of MATLAB simulations of medical image registration approached through these algorithms have been analyzed. The simulation shows that the dragonfly algorithm results in higher quality image registration, but takes longer to converge. The trade-off issue between the quality of registration and the computing time has been brought forward. This has a strong impact on the choice of the most suitable algorithm for medical applications, such as monitoring of tumor progression.

Keywords

Artificial bee colony algorithm Dragonfly algorithm Medical image registration Particle swarm optimization algorithm Swarm intelligence 

Notes

Acknowledgements

Authors acknowledge with gratitude the support received from REVA University, Bengaluru, and M. S. Ramaiah University of Applied Sciences, Bengaluru. They also express sincere thanks to the anonymous reviewers of this article for their constructive criticism.

References

  1. 1.
    Rueckert, D., Schnabel, J.A.: Registration and segmentation in medical imaging. In: Cipolla R., Battiato, S., Farinella, G. (eds.), Registration and Recognition in Images and Videos, volume 532 of Studies in Computational Intelligence, 137–156. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  2. 2.
    Peressutti, D., Gómez, A., Penney, G.P., King, A.P.: Registration of multiview echocardiography sequences using a subspace error metric. IEEE Trans. Biomed. Eng. 64(2), 352–361 (2017)CrossRefGoogle Scholar
  3. 3.
    Xu, R., Athavale, P., Nachman, A., Wright, G.A.: Multiscale registration of real-time and prior MRI data for image-guided cardiac interventions. IEEE Trans. Biomed. Eng. 61(10), 2621–2632 (2014)CrossRefGoogle Scholar
  4. 4.
    Kang, X., Armand, M., Otake, Y., Yau, W.P., Cheung, P.Y., Hu, Y., Taylor, R.H.: Robustness and accuracy of feature-based single image 2-D to 3-D registration without correspondences for image-guided intervention. IEEE Trans. Biomed. Eng. 61(1), 149–161 (2014)CrossRefGoogle Scholar
  5. 5.
    Ebrahimi, M., Kulaseharan, S.: Deformable image registration and intensity correction of cardiac perfusion MRI. In: Proceedings of the 5th International Workshop Statistical Atlases and Computational Models of the Heart-Imaging and Modelling Challenges, Revised Selected Papers, pp. 13–20. Springer, Cham (2015)Google Scholar
  6. 6.
    Tagare, H.D., Rao, M.: Why does mutual-information work for image registration? A deterministic explanation. IEEE Trans. Pattern Anal. Mach. Intell. 37(6), 1286–1296 (2015)CrossRefGoogle Scholar
  7. 7.
    Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948, Nov 1995Google Scholar
  8. 8.
    Karaboga, D., Akay, B.: A modified artificial bee colony (ABC) algorithm for constrained optimization problems. 11(3), 3021–3031 (2011)Google Scholar
  9. 9.
    Mirjalili, S.: Dragonfly algorithm: a new meta-heuristic optimization technique for solving single-objective, discrete, and multi-objective problems. Neural Comput. Appl. 27, 1053–1073 (2015)CrossRefGoogle Scholar
  10. 10.
    Sotiras, A., Davatzikos, C., Paragios, N.: Deformable medical image registration: a survey. IEEE Trans. Med. Imaging 32(7), 1153–1190 (2013)CrossRefGoogle Scholar
  11. 11.
    Bermejo, E., Cordón, O., Damas, S., Santamaría, J.: A comparative study on the application of advanced bacterial foraging models to image registration. Inf. Sci. 295, 160–181 (2015)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Damas, S., Cordon, O., Santamaria, J.: Medical image registration using evolutionary computation: an experimental survey. IEEE Comput. Intell. Mag. 6(4), 26–42 (2011)CrossRefGoogle Scholar
  13. 13.
    Schwab, L., Schmitt, M., Wanka, R.: Multimodal medical image registration using particle swarm optimization with influence of the data’s initial orientation. In: Proceedings of the IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB), pp. 1–8, Aug 2015Google Scholar
  14. 14.
    Kulkarni, R.V., Venayagamoorthy, G.K.: Particle swarm optimization in wireless-sensor networks: a brief survey. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 41(2), 262–267 (2011)CrossRefGoogle Scholar
  15. 15.
    Akay, B., Karaboga, D.: A survey on the applications of artificial bee colony in signal, image, and video processing. SIViP 9(4), 967–990 (2015)CrossRefGoogle Scholar
  16. 16.
    Brajevic, I.: Crossover-based artificial bee colony algorithm for constrained optimization problems. Neural Comput. Appl. 26(7), 1587–1601 (2015)CrossRefGoogle Scholar
  17. 17.
    Kulkarni, V.R., Desai, V., Kulkarni, R.V.: Multistage localization in wireless sensor networks using artificial bee colony algorithm. In: Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1–8, Dec 2016Google Scholar
  18. 18.
    Wells, W.M., Viola, P.A., Atsumi, H., Nakajima, S., Kikinis, R.: Multi-modal volume registration by maximization of mutual information. Med. Image Anal. 1(1), 35–51 (1996)CrossRefGoogle Scholar
  19. 19.
    Ganesan, S.I., Manickam, C., Raman, G.R., Raman, G.P.: Dragonfly algorithm based global maximum power point tracker for photovoltaic systems. In: International Conference in Swarm Intelligence, pp. 211–219. Springer, Cham (2016)Google Scholar
  20. 20.
    Murugan, S.: Memory based hybrid dragonfly algorithm for numerical optimization problems. Expert Syst. Appl. 83, 63–78 (2017)CrossRefGoogle Scholar
  21. 21.
    Suresh, V., Sreejith, S.V.: Generation dispatch of combined solar thermal systems using dragonfly algorithm. Computing 99, 59–80 (2016)MathSciNetCrossRefzbMATHGoogle Scholar
  22. 22.
    Brajevic, I., Tuba, M.: An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems. J. Intell. Manuf. 24(4), 729–740 (2013)CrossRefGoogle Scholar
  23. 23.
    Damas, S., Cordón, O., Santamaria, J.: Medical image registration using evolutionary computation: an experimental survey. IEEE Comput. Intell. Mag. 6(4), 26–42 (2011)CrossRefGoogle Scholar
  24. 24.
    Kulkarni, R.V., Venayagamoorthy, G.K.: Bio-inspired algorithms for autonomous deployment and localization of sensor nodes. IEEE Trans. Syst. Man Cybern Part C Appl. Rev. 40(6), 663–675 (2010)CrossRefGoogle Scholar
  25. 25.
    De Leon-Aldaco, S.E., Calleja, H., Alquicira, J.A.: Metaheuristic optimization methods applied to power converters: a review. IEEE Trans. Power Electron. 30(12), 6791–6803 (2015)CrossRefGoogle Scholar
  26. 26.
    The National Library of Medicine MedPix. https://medpix.nlm.nih.gov/home

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.REVA UniversityBengaluruIndia
  2. 2.M. S. Ramaiah University of Applied SciencesBengaluruIndia

Personalised recommendations