Social Spider Optimization Algorithm in Multimodal Medical Image Registration
محورهای موضوعی : پردازش تصویر و ویدیو، پردازش گفتار
Zahra Hossein-Nejad
1
,
Mehdi Nasri
2
1 - Department of Electrical Engineering, Sirjan brnach, Islamic Azad University Sirjan, Iran
2 - Department of Biomedical Engineering, Khomeinishahr branch, Islamic Azad University Isfahan,
کلید واژه: image registration, medical image processing, optimization, meta-heuristic algorithms, Social Spider Optimization.,
چکیده مقاله :
Medical image registration plays an important role in many clinical applications, including the detection and diagnosis of diseases, planning of therapy, guidance of interventions. Multimodal medical image registration is the process of overlapping two or more images taken from the same scene by different modalities and different sensors. Intensity-based methods are widely used in multimodal medical image registration, these techniques register different modality images that have the same content by optimal transformation. The estimation of the optimal transformation requires the optimization of a similarity metric between the images. Recently, various optimization algorithms have been presented that the selection of appropriate optimization algorithms is very important in determining the optimal transformation parameter. The Social Spider Optimization (SSO) algorithm is one of the meta-heuristic methods that prevents premature convergence. In this paper, medical image registration technique is suggested based on the SSO algorithm. The Mutual Information (MI), Normalization of Mutual Information (NMI), and Sum of Squared Differences (SSD) are used separately as cost function (objective function) and the performance of each of these functions is checked in multimodal medical image registration. The simulation results on Brain Web data set affirm the suggested method outperforms classical registration methods in terms of convergence rate, execution time.
Medical image registration plays an important role in many clinical applications, including the detection and diagnosis of diseases, planning of therapy, guidance of interventions. Multimodal medical image registration is the process of overlapping two or more images taken from the same scene by different modalities and different sensors. Intensity-based methods are widely used in multimodal medical image registration, these techniques register different modality images that have the same content by optimal transformation. The estimation of the optimal transformation requires the optimization of a similarity metric between the images. Recently, various optimization algorithms have been presented that the selection of appropriate optimization algorithms is very important in determining the optimal transformation parameter. The Social Spider Optimization (SSO) algorithm is one of the meta-heuristic methods that prevents premature convergence. In this paper, medical image registration technique is suggested based on the SSO algorithm. The Mutual Information (MI), Normalization of Mutual Information (NMI), and Sum of Squared Differences (SSD) are used separately as cost function (objective function) and the performance of each of these functions is checked in multimodal medical image registration. The simulation results on Brain Web data set affirm the suggested method outperforms classical registration methods in terms of convergence rate, execution time.
[1] Z. Hossein-Nejad and M. Nasri, "Adaptive RANSAC and extended region-growing algorithm for object recognition over remote-sensing images," Multimedia Tools and Applications, 2022.
[2] Z. Hossein-Nejad and M. Nasri, "Clustered redundant keypoint elimination method for image mosaicing using a new Gaussian-weighted blending algorithm," The Visual Computer, 2021/07/19 2021.
[3] Z. hossein-nejad and M. Nasri, "Clustered Redundant Keypoint Elimination SURF method in MRI Image Registration based on Alpha-Trimmed Relationship," Frontiers in Biomedical Technologies, 2022.
[4] C.-L. Cocianu, C. R. Uscatu, and A. D. Stan, "Evolutionary Image Registration: A review," Sensors, vol. 23, p. 967, 2023.
[5] Z. Hossein-Nejad and M. Nasri, "Image Registration Based on Redundant Keypoint Elimination SARSIFT Algorithm and MROGH Descriptor," in 2022 International Conference on Machine Vision and Image Processing (MVIP), 2022, pp. 1-5.
[6] Z. Hossein-Nejad and M. Nasri, "Retinal Image Registration based on Auto-Adaptive SIFT and Redundant Keypoint Elimination Method," in 2019 27th Iranian Conference on Electrical Engineering (ICEE), 2019, pp. 1294-1297.
[7] C. Wang, L. Xu, R. Xu, S. Xu, W. Meng, R. Wang, et al., "Triple Robustness Augmentation Local Features for multi-source image registration," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 199, pp. 1-14, 2023.
[8] L. Rundo, A. Tangherloni, C. Militello, M. C. Gilardi, and G. Mauri, "Multimodal medical image registration using particle swarm optimization: a review," in 2016 IEEE Symposium Series on Computational Intelligence (SSCI), 2016, pp. 1-8.
[9] K. Shaw, P. Pandey, S. Das, D. Ghosh, P. Malakar, and S. Dhabal, "Image Registration using Bio-inspired Algorithms," in 2020 IEEE 1st International Conference for Convergence in Engineering (ICCE), 2020, pp. 330-334.
[10] H. Rezaei, M. Shakeri, S. Azadi, and K. Jaferzade, "Multimodality image registration utilizing ant colony algorithm," in 2009 Second International Conference on Machine Vision, 2009, pp. 49-53.
[11] M. P. Wachowiak, R. Smolíková, Y. Zheng, J. M. Zurada, and A. S. Elmaghraby, "An approach to multimodal biomedical image registration utilizing particle swarm optimization," IEEE Transactions on evolutionary computation, vol. 8, pp. 289-301, 2004.
[12] Y. Wu, W. Ma, Q. Miao, and S. Wang, "Multimodal continuous ant colony optimization for multisensor remote sensing image registration with local search," Swarm and Evolutionary Computation, vol. 47, pp. 89-95, 2019.
[13] Y. Tian and H. D. Ma, "Image registration based on improved ant colony algorithm," in Advanced Materials Research, 2013, pp. 683-686.
[14] Y. Zhuang, K. Gao, X. Miu, L. Han, and X. Gong, "Infrared and visual image registration based on mutual information with a combined particle swarm optimization–Powell search algorithm," Optik, vol. 127, pp. 188-191, 2016.
[15] H. Ghorbani and A. A. Beheshti, "Multiresolution registration of multitemporal remote sensing images by optimization of mutual information using a simulated annealing based Marquardt-Levenberg technique," in 2007 International Conference on Intelligent and Advanced Systems, 2007, pp. 685-690.
[16] I. Maglogiannis and E. Zafiropoulos, "Automated medical image registration using the simulated annealing algorithm," in Hellenic Conference on Artificial Intelligence, 2004, pp. 456-465.
[17] H. Zhang, X. Zhou, J. Sun, and J. Zhang, "A novel medical image registration method based on mutual information and genetic algorithm," in International Conference on Computer Graphics, Imaging and Visualization (CGIV'05), 2005, pp. 221-226.
[18] R. Suganya and S. Rajaram, "Mutual information and genetic algorithm based registration of MRI brain images," Canadian Journal on Biomedical Engineering and Technology, vol. 2, pp. 14-17, 2011.
[19] G. K. Matsopoulos, N. A. Mouravliansky, K. K. Delibasis, and K. S. Nikita, "Automatic retinal image registration scheme using global optimization techniques," IEEE Transactions on Information Technology in Biomedicine, vol. 3, pp. 47-60, 1999.
[20] S. Chakraborty, R. Pradhan, A. S Ashour, L. Moraru, and N. Dey, "Grey-Wolf-Based Wang’s Demons for retinal image registration," Entropy, vol. 22, p. 659, 2020.
[21] S. Chakraborty, N. Dey, S. Samanta, A. S. Ashour, and V. E. Balas, "Firefly algorithm for optimized nonrigid demons registration," in Bio-inspired computation and applications in image processing, ed: Elsevier, 2016, pp. 221-237.
[22] C.-L. Cocianu and A. Stan, "New evolutionary-based techniques for image registration," Applied Sciences, vol. 9, p. 176, 2019.
[23] Y. Qin, H. Hu, Y. Shi, Y. Liu, and H. Gao, "An artificial bee colony algorithm hybrid with differential evolution for multi-temporal image registration," in 2016 35th Chinese Control Conference (CCC), 2016, pp. 2734-2739.
[24] D. Sarvamangala and R. V. Kulkarni, "Swarm intelligence algorithms for medical image registration: A comparative study," in International Conference on Computational Intelligence, Communications, and Business Analytics, 2017, pp. 451-465.
[25] Y. Zhang and L. Wu, "Rigid image registration by PSOSQP algorithm," Advances in Digital Multimedia, vol. 1, pp. 4-8, 2012.
[26] E. Cuevas, M. Cienfuegos, D. Zaldívar, and M. Pérez-Cisneros, "A swarm optimization algorithm inspired in the behavior of the social-spider," Expert Systems with Applications, vol. 40, pp. 6374-6384, 2013.
[27] L. Maurya, P. K. Mahapatra, and A. Kumar, "A social spider optimized image fusion approach for contrast enhancement and brightness preservation," Applied Soft Computing, vol. 52, pp. 575-592, 2017.
[28] R. Vijay and V. Priya, "Anti-islanding protection of distributed generation based on social spider optimization technique," International Journal of Advanced Engineering Research and Science, vol. 4, p. 237193, 2017.
[29] A. Hosseinalipour, F. S. Gharehchopogh, M. Masdari, and A. Khademi, "Toward text psychology analysis using social spider optimization algorithm," Concurrency and Computation: Practice and Experience, vol. 33, p. e6325, 2021.
[30] D. R. Pereira, M. A. Pazoti, L. A. Pereira, D. Rodrigues, C. O. Ramos, A. N. Souza, et al., "Social-Spider Optimization-based Support Vector Machines applied for energy theft detection," Computers & Electrical Engineering, vol. 49, pp. 25-38, 2016.
[31] J. Kennedy and R. Eberhart, "Particle swarm optimization," in Proceedings of ICNN'95-international conference on neural networks, 1995, pp. 1942-1948.
[32] M. Kumar, M. Husain, N. Upreti, and D. Gupta, "Genetic algorithm: Review and application," Available at SSRN 3529843, 2010.
[33] A. H. Gandomi, X.-S. Yang, and A. H. Alavi, "Cuckoo search algorithm: a metaheuristic approach to solve structural optimization problems," Engineering with computers, vol. 29, pp. 17-35, 2013.
[34] D. Karaboga and B. Basturk, "A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm," Journal of global optimization, vol. 39, pp. 459-471, 2007.
[35] X.-S. Yang, "Harmony search as a metaheuristic algorithm," in Music-inspired harmony search algorithm, ed: Springer, 2009, pp. 1-14.
[36] A. Luque-Chang, E. Cuevas, F. Fausto, D. Zaldivar, and M. Pérez, "Social spider optimization algorithm: modifications, applications, and perspectives," Mathematical Problems in Engineering, vol. 2018, 2018.
[37] R. Panda, S. Agrawal, M. Sahoo, and R. Nayak, "A novel evolutionary rigid body docking algorithm for medical image registration," Swarm and Evolutionary Computation, vol. 33, pp. 108-118, 2017.
[38] R.-S. Kwan, A. C. Evans, and G. B. Pike, "MRI simulation-based evaluation of image-processing and classification methods," IEEE transactions on medical imaging, vol. 18, pp. 1085-1097, 1999.
