Unimodal Medical Image Registration using Elite Opposition Bacterial Foraging Optimization Algorithm

Danlami Maliki, Muazu Mohammed Bashir, Jonathan Gana Kolo, Olaniyi Olayemi Mikail

Abstract


Medical imaging applications frequently use image registration for a variety of purposes, and the search of an ideal image transformation parameters that align the two images (reference and floating) is still an optimization challenge. Medical image registration has been optimized using different metaheuristics optimization strategies.  One method, the Bacterial Foraging Algorithm (BFOA), has issues of poor exploration and low convergence to a better solution. This research work presents the Elite Opposition Bacterial Foraging Optimization Algorithm (EOBFOA) for optimizing unimodal medical image registration. The EOBFOA is an enhanced version of Bacterial Foraging Algorithm (BFOA) using the Elite Opposition Strategy. The proposed EOBFOA uses Root Mean Square Error (RMSE) as a measure to determine the accuracy of the image registration process. The performance of the image registration using the EOBFOA was compared against other existing nature inspired algorithms. The obtained results shown that the proposed EOBFOA outperformed other algorithms in searching for the best optimum transformation parameters for the image registration.


Full Text:

PDF

References


Abiyev, R. H., & Tunay, M. (2016). Experimental study of specific benchmarking functions for modified Monkey algorithm. Procedia - Procedia Computer Science, 102(August), 595–602. https://doi.org/10.1016/j.procs.2016.09.448

Alkinani, M. H. (2021). Effects of Lossy Image Compression on Medical Image Registration Accuracy. 1–4.

Andrade, N., Faria, F. A., & Cappabianco, F. A. M. (2019). A Practical Review on Medical Image Registration: From Rigid to Deep Learning Based Approaches. Proceedings - 31st Conference on Graphics, Patterns and Images, SIBGRAPI 2018, 463–470. https://doi.org/10.1109/SIBGRAPI.2018.00066

Bejinariu, S. I., Rotariu, C., Costin, H., & Luca, R. (2019). Image Registration using Fireworks Algorithm and Chaotic Sequences. 2019 11th International Symposium on Advanced Topics in Electrical Engineering, ATEE 2019, 1–4. https://doi.org/10.1109/ATEE.2019.8725020

Bejinariu, S., & Luca, R. (2016). Nature-inspired Algorithms based Multispectral Image Fusion. Epe, 20–22.

Charif, F., Benchabane, A., & Bebboukha, Z. (2019). Multimodal Medical Images Registration Using Biogeography-Based Optimization Algorithm. Proceedings - 2019 6th International Conference on Image and Signal Processing and Their Applications, ISPA 2019, 6–10. https://doi.org/10.1109/ISPA48434.2019.8966862

Chen, H., Wang, L., Di, J., & Ping, S. (2020). Bacterial Foraging Optimization Based on Self-Adaptive Chemotaxis Strategy. Computational Intelligence and Neuroscience, 2020. https://doi.org/10.1155/2020/2630104

Chen, Y., Li, Y., Wang, G., Zheng, Y., Xu, Q., Fan, J., & Cui, X. (2017). PT US CR. Expert Systems With Applications. https://doi.org/10.1016/j.eswa.2017.04.019

Dida, H., Charif, F., & Benchabane, A. (2020). Grey Wolf Optimizer for Multimodal Medical Image Registration. 4th International Conference on Intelligent Computing in Data Sciences, ICDS 2020, 1, 0–4. https://doi.org/10.1109/ICDS50568.2020.9268771

Guo, C., Tang, H., Niu, B., Boon, C., & Lee, P. (2021). Neurocomputing A survey of bacterial foraging optimization. Neurocomputing, 452, 728–746. https://doi.org/10.1016/j.neucom.2020.06.142

Li, J. (2014). Analysis and Improvement of the Bacterial Foraging Optimization Algorithm. 8(1), 1–10.

Imam, M. L., Adebiyi, B. H., Bello-Salau, H., Olarinoye, G. A., & Momoh, M. O. (2019, October). Cultured Artificial Fish Swarm Algorithm: An Experimental Evaluation. In 2019 2nd International Conference of the IEEE Nigeria Computer Chapter (NigeriaComputConf) (pp. 1-7). IEEE.

Sara, U., Akter, M., & Uddin, M. S. (2019). Image Quality Assessment through FSIM, SSIM, MSE and PSNR—A Comparative Study. Journal of Computer and Communications, 07(03), 8–18. https://doi.org/10.4236/jcc.2019.73002

Sasithradri, A., & Nirmal S.N. (2014). Synergy of Adaptative Bacterial Foraging Algorithm and Particle Swarm Optimization Algorithm for Image Segmentation. 2014. International Conference on Circuit, Power and Computing Technologies [ICCPCT], Pp 1503-1506.

Singh, A. (2014). A Systematic way for Image Segmentation based on Bacteria Foraging Optimization Technique ( Its implementation and analysis for image segmentation ). 5(1), 130–133.

Tuba, E., Strumberger, I., Zivkovic, D., Bacanin, N., & Tuba, M. (2018). Rigid Image Registration by Bare Bones Fireworks Algorithm. International Conference on Multimedia Computing and Systems -Proceedings, 2018-May, 1–6. https://doi.org/10.1109/ICMCS.2018.8525968

Zhang, L., Liu, L., Yang, X., & Dai, Y. (2016). A Novel Hybrid Firefly Algorithm for Global Optimization. 1–17. https://doi.org/10.1371/journal.pone.0163230


Refbacks

  • There are currently no refbacks.