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Swarm Intelligence Algorithms for Medical Image Registration: A Comparative Study

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Computational Intelligence, Communications, and Business Analytics (CICBA 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 776))

Abstract

The search for 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 paper using the particle swarm optimization algorithm and the artificial bee colony algorithm (ABC). Brief introductions to these algorithms have been presented. Results of Matlab simulations of medical image registration approached through these algorithms have been analyzed. The results show that the ABC algorithm results in higher quality of image registration; but, takes longer to converge. The tradeoff 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 a specific medical application.

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References

  1. Rueckert, D., Schnabel, J.A.: Registration and segmentation in medical imaging. In: Cipolla, R., Battiato, S., Farinella, G.M. (eds.) Registration and Recognition in Images and Videos. SCI, vol. 532, pp. 137–156. Springer, Heidelberg (2014). doi:10.1007/978-3-642-44907-9_7

    Chapter  Google Scholar 

  2. 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)

    Article  Google Scholar 

  3. 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)

    Article  Google Scholar 

  4. Kang, X., Armand, M., Otake, Y., Yau, W.-P., Cheung, P.Y.S., 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)

    Article  Google Scholar 

  5. Li, B., Tian, L., Ou, S.: Rapid multimodal medical image registration and fusion in 3-D conformal radiotherapy treatment planning. In Proceedings of the 4th International Conference on Bioinformatics and Biomedical Engineering (iCBBE), pp. 1–5, June 2010

    Google Scholar 

  6. Ebrahimi, M., Kulaseharan, S.: Deformable image registration and intensity correction of cardiac perfusion MRI. In: Camara, O., Mansi, T., Pop, M., Rhode, K., Sermesant, M., Young, A. (eds.) STACOM 2014. LNCS, vol. 8896, pp. 13–20. Springer, Cham (2015). doi:10.1007/978-3-319-14678-2_2

    Google Scholar 

  7. Shenoy, R., Shih, M.-C., Rose, K.: Deformable registration of biomedical images using 2-D hidden Markov models. IEEE Trans. Image Process. 25(10), 4631–4640 (2016)

    Article  MathSciNet  Google Scholar 

  8. 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)

    Article  Google Scholar 

  9. Yang, F., Ding, M., Zhang, X., Hou, W., Zhong, C.: Non-rigid multi-modal medical image registration by combining L-BFGS-B with cat swarm optimization. Inf. Sci. 316, 440–456 (2015)

    Article  Google Scholar 

  10. Mitchell, M.: An Introduction to Genetic Algorithms. MIT Press, Cambridge (1998)

    MATH  Google Scholar 

  11. Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, vol. 4, pp. 1942–1948 (1995)

    Google Scholar 

  12. Karaboga, D., Akay, B.: A modified artificial bee colony (ABC) algorithm for constrained optimization problems. Appl. Soft Comput. 11(3), 3021–3031 (2011)

    Article  Google Scholar 

  13. 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)

    Article  Google Scholar 

  14. Cole-Rhodes, A.A., Johnson, K.L., Le Moigne, J., Zavorin, I.: Multiresolution registration of remote sensing imagery by optimization of mutual information using a stochastic gradient. IEEE Trans. Image Process. 12(12), 1495–1511 (2003)

    Article  MathSciNet  Google Scholar 

  15. Pradhan, S., Patra, D.: Enhanced mutual information based medical image registration. IET Image Proc. 10(5), 418–427 (2016)

    Article  Google Scholar 

  16. Sotiras, A., Davatzikos, C., Paragios, N.: Deformable medical image registration: a survey. IEEE Trans. Med. Imaging 32(7), 1153–1190 (2013)

    Article  Google Scholar 

  17. 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)

    Article  MathSciNet  Google Scholar 

  18. Damas, S., Cordon, O., Santamaria, J.: Medical image registration using evolutionary computation: an experimental survey. IEEE Comput. Intell. Mag. 6(4), 26–42 (2011)

    Article  Google Scholar 

  19. 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 (2015)

    Google Scholar 

  20. 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)

    Article  Google Scholar 

  21. 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)

    Article  Google Scholar 

  22. Brajevic, I.: Crossover-based artificial bee colony algorithm for constrained optimization problems. Neural Comput. Appl. 26(7), 1587–1601 (2015)

    Article  Google Scholar 

  23. 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, December 2016

    Google Scholar 

  24. Damas, S., Cordón, O., Santamaria, J.: Medical image registration using evolutionary computation: an experimental survey. IEEE Comput. Intell. Mag. 6(4), 26–42 (2011)

    Article  Google Scholar 

  25. 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)

    Article  Google Scholar 

  26. De Leon-Aldaco, S.E., Calleja, H., Aguayo Alquicira, J.: Metaheuristic optimization methods applied to power converters: a review. IEEE Trans. Power Electron. 30(12), 6791–6803 (2015)

    Article  Google Scholar 

  27. Pi, Q., Ye, H.: Survey of particle swarm optimization algorithm and its applications in antenna circuit. In: Proceedings of the IEEE International Conference on Communication Problem-Solving (ICCP), pp. 492–495, October 2015

    Google Scholar 

  28. Karaboga, D., Basturk, B.: A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm. J. Glob. Optim. 39(3), 459–471 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  29. Brajevic, I., Tuba, M.: An upgraded artificial bee colony (ABC) algorithm for constrained optimization problems. J. Intell. Manuf. 24(4), 729–740 (2013)

    Article  Google Scholar 

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Acknowledgment

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 paper for their constructive criticism.

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Authors and Affiliations

  1. REVA University, Bengaluru, India

    D. R. Sarvamangala

  2. M.S. Ramaiah University of Applied Sciences, Bengaluru, India

    Raghavendra V. Kulkarni

Authors
  1. D. R. Sarvamangala
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  2. Raghavendra V. Kulkarni
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Corresponding author

Correspondence to D. R. Sarvamangala .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India

    J. K. Mandal

  2. Department of Computer and System Sciences, Visva Bharati University, Bolpur Santiniketan, West Bengal, India

    Paramartha Dutta

  3. Department of Information Technology, Calcutta Business School, Kolkata, India

    Somnath Mukhopadhyay

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Sarvamangala, D.R., Kulkarni, R.V. (2017). Swarm Intelligence Algorithms for Medical Image Registration: A Comparative Study. In: Mandal, J., Dutta, P., Mukhopadhyay, S. (eds) Computational Intelligence, Communications, and Business Analytics. CICBA 2017. Communications in Computer and Information Science, vol 776. Springer, Singapore. https://doi.org/10.1007/978-981-10-6430-2_35

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  • DOI: https://doi.org/10.1007/978-981-10-6430-2_35

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-6429-6

  • Online ISBN: 978-981-10-6430-2

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