Enhancing Medical Imaging with Swarm Intelligence Algorithms
DOI:
https://doi.org/10.31185/wjcms.232Abstract
Medical imaging serves as an indispensable tool for the diagnosis and continuous monitoring of a diverse array of health conditions. A recent and exciting development in this field is the integration of Swarm Intelligence (SI) algorithms, which draw inspiration from the collective behaviors observed in social insects. This collaborative effort between nature and technology is progressively transforming medical image analysis, elevating both its quality and efficiency. In this book chapter we have presented various SI optimization algorithms like ACO, BCO, FA, FSA and WOA in detail. By exploring these algorithms, we aim to provide an in-depth understanding of their respective benefits and limitations when applied to medical image analysis. This knowledge empowers practitioners to choose the most appropriate algorithm for specific tasks, ensuring optimal outcomes. Furthermore, we shed light on SI-Based Segmentation methodologies, elucidating the advantages and constraints associated with these approaches. The ability of SI algorithms to innovate in the realms of image segmentation, feature extraction, and classification is emphasized, with a focus on their potential to enhance diagnostic accuracy and elevate the quality of patient care. One of the most exciting prospects on the horizon is the amalgamation of SI with cutting-edge technologies like deep learning and big data analytics. This union has the potential to revolutionize medical imaging by providing solutions that are not only more accurate and efficient but also highly clinically relevant. As these developments continue to unfold, the synergy between SI and emerging technologies promises to reshape the medical imaging landscape, ultimately enhancing patient care and improving healthcare outcomes in unprecedented ways
References
G. Beni and J. Wang, “Swarm intelligence in cellular robotic systems,” in Robots and biological systems: towards a new bionics? (pp, pp. 703–712, Springer, 1993.
J. Kennedy, Swarm intelligence. In Handbook of nature-inspired and innovative computing: integrating classical models with emerging technologies. Boston, MA: Springer US, 2006.
M. Dorigo, M. Birattari, S. Garnier, H. Hamann, M. M. D. Oca, C. Solnon, and T. Stützle Swarm intelligence. Scholarpedia, vol. 2, pp. 1462–1462, 2007.
B. K. Panigrahi, Y. Shi, and M. H. Lim Handbook of Swarm Intelligence. Series: Adaptation, Learning, and Optimization, 2011.
M. Dorigo, E. Bonabeau, and G. Theraulaz Ant algorithms and stigmergy. Future generation computer systems, vol. 16, pp. 851–871, 2000.
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.
E. S. Olivas, J. D. M. Guerrero, M. Martinez-Sober, J. R. Magdalena-Benedito, and Serrano, Handbook of research on machine learning applications and trends: Algorithms, methods, and techniques: Algorithms, methods, and techniques. IGI global, 2009.
S. Das, B. K. Panigrahi, and S. S. Pattnaik, “Nature inspired methods for multi-objective optimization,” Handbook of Research on Machine Learning Applications and Trends: Algorithms, Methods, and Techniques, pp. 95–108, 2010.
S. Das, A. Abraham, and A. Konar, “Swarm intelligence algorithms in bioinformatics,” in Computational Intelligence in Bioinformatics, pp. 113– 147, Springer, 2008.
M. M. Soliman, A. E. Hassanien, N. I. Ghali, and H. M. Onsi, “An adaptive watermarking approach for medical imaging using swarm intelligent,” International Journal of Smart Home, vol. 6, no. 1, pp. 37–50, 2012.
Q. T. Bui, Q. H. Nguyen, X. L. Nguyen, V. D. Pham, H. D. Nguyen, and V. M. Pham, “Verification of novel integrations of swarm intelligence algorithms into deep learning neural network for flood susceptibility mapping,” Journal of Hydrology, vol. 581, pp. 124379–124379, 2020.
Z. Zhang, K. Long, J. Wang, and F. Dressler, “On swarm intelligence inspired self-organized networking: its bionic mechanisms, designing principles and optimization approaches,” IEEE Communications Surveys & Tutorials, vol. 16, no. 1, pp. 513–537, 2013.
A. Chakraborty and A. K. Kar Swarm intelligence: A review of algorithms. Nature-inspired computing and optimization: Theory and applications, pp. 475–494, 2017.
G. Venter and J. Sobieszczanski-Sobieski, “Particle swarm optimization,” AIAA journal, no. 8, pp. 1583–1589, 2003.
Q. Gong, X. Zhao, C. Bi, L. Chen, X. Nie, P. Wang, J. Zhan, Q. Li, and W. Gao, “Maximum entropy multi-threshold image segmentation based on improved particle swarm optimization,” Journal of Physics: Conference Series, pp. 12098–12098, 2020.
H. Allioui, M. Sadgal, and A. Elfazziki, “Optimized control for medical image segmentation: Improved multi-agent systems agreements using Particle Swarm Optimization,” J. Ambient Intell. Humaniz. Comput. 2021, vol. 12, pp. 8867–8885.
J. Zhao, X. Wang, and M. Li, “A novel Neutrosophic image segmentation based on improved fuzzy C-means algorithm (NIS-IFCM),” Int. J. Pattern Recognit. Artif. Intell, vol. 34, pp. 2055011–2055011, 2020.
N. Kumar and H. Kumar A fuzzy clustering technique for enhancing the convergence performance by using improved Fuzzy c-means and Particle Swarm Optimization algorithms. Data Knowl, 2022.
L. Zhang and C. P. Lim, “Intelligent optic disc segmentation using improved particle swarm optimization and evolving ensemble models,” Appl. Soft Comput, pp. 106328–106328, 2020.
H. Liang and J. Zou, “Rock image segmentation of improved semi-supervised SVMs-FCM algorithm based on chaos,” Circuits Syst. Signal Process, vol. 39, pp. 571–585, 2020.
T. R. Farshi, J. H. Drake, and E. Özcan, “A multimodal particle swarm optimization-based approach for image segmentation,” Expert Syst. Appl, pp. 113233–113233, 2020.
D. Oliva, M. A. Elaziz, and S. Hinojosa, “Multilevel thresholding for image segmentation based on metaheuristic algorithms,” in Metaheuristic Algorithms for Image Segmentation: Theory and Applications, pp. 59–69, Springer, 2019.
L. Yu, Y. Han, and L. Mu, “Improved quantum evolutionary particle swarm optimization for band selection of hyperspectral image,” Remote Sens. Lett, vol. 11, pp. 866–875, 2020.
Z. Wu, Z. Wu, and J. Zhang, “An improved FCM algorithm with adaptive weights based on SA-PSO,” Neural Comput. Appl, vol. 28, pp. 3113– 3118, 2017.
A. Darwish, D. Ezzat, and A. E. Hassanien, “An optimized model based on convolutional neural networks and orthogonal learning particle swarm optimization algorithm for plant diseases diagnosis,” Swarm Evol. Comput, vol. 52, pp. 100616–100616, 2020.
P. K. Muhuri, Z. Ashraf, and S. Goel, “A novel image steganographic method based on integer wavelet transformation and particle swarm optimization,” Appl. Soft Comput, vol. 92, pp. 106257–106257, 2020.
D. Kar, M. Ghosh, R. Guha, R. Sarkar, L. Garcia-Hernandez, and A. Abraham, “Fuzzy mutation embedded hybrids of gravitational search and Particle Swarm Optimization methods for engineering design problems,” Eng. Appl. Artif. Intell, vol. 2020, pp. 103847–103847.
J. T. Zhu, C. F. Gong, M. X. Zhao, L. Wang, and Y. Luo, “Image mosaic algorithm based on PCA-ORB feature matching. The International Archives of Photogrammetry,” Remote Sens. Spat. Inf. Sci, vol. 2020, pp. 83–89.
U. R. Acharya, H. Fujita, V. K. Sudarshan, S. L. Oh, M. Adam, and J. Tan Automated characterization of coronary artery disease, myocardial infarction, and congestive heart failure using contourlet and shearlet transforms of electrocardiogram signal, vol. 132, pp. 156–166, 2017.
C. Wang, F. Wu, Z. Shi, and D. Zhang, “Indoor positioning technique by combining RFID and particle swarm optimization-based back propagation neural network,” Optik, vol. 127, pp. 6839–6849, 2016.
D. Zhang, J. Wang, H. Fan, T. Zhang, J. Gao, and P. Yang, “New method of traffic flow forecasting based on quantum particle swarm optimization strategy for intelligent transportation system,” Int. J. Commun. Syst, vol. 34, pp. 4647–4647, 2021.
S. Jeevitha and N. A. Prabha, “Effective payload and improved security using HMT Contourlet transform in medical image steganography,” Health Technol, vol. 10, pp. 217–229, 2020.
Z. Hu, L. Wang, L. Qi, Y. Li, and W. Yang, “A novel wireless network intrusion detection method based on adaptive synthetic sampling and an improved convolutional neural network,” IEEE Access, vol. 8, pp. 195741–195751, 2020.
Z. Tian, Y. Ren, and G. Wang, “Short-term wind speed prediction based on improved PSO algorithm optimized EM-ELM. Energy Sources Part A Recovery Util,” Environ. Eff, vol. 41, pp. 26–46, 2019.
E. Hadavandi, S. Mostafayi, and P. Soltani, “A Grey Wolf Optimizer-based neural network coupled with response surface method for modeling the strength of siro-spun yarn in spinning mills,” Appl. Soft Comput, vol. 72, pp. 1–13, 2018.
S. H. Moghaddam, A. Mokhtarzade, M. Beirami, and B. A, “A feature extraction method based on spectral segmentation and integration of hyperspectral images,” Int. J. Appl. Earth Obs. Geoinf, vol. 89, pp. 102097–102097, 2020.
R. Pati, A. K. Pujari, and P. Gahan, “Face recognition using particle swarm optimization based block ICA,” Multimed. Tools Appl, vol. 80, pp. 35685–35695, 2021.
J. Too, A. R. Abdullah, and N. Saad Hybrid binary particle swarm optimization differential evolution-based feature selection for EMG signals classification, vol. 8, pp. 79–79, 2019.
W. Ji, G. Chen, B. Xu, X. Meng, and D. Zhao, “Recognition method of green pepper in greenhouse based on least-squares support vector machine optimized by the improved particle swarm optimization,” IEEE Access, vol. 7, pp. 119742–119754, 2019.
Y. Tang, L. He, W. Lu, X. Huang, H. Wei, and H. Xiao, “A novel approach for fracture skeleton extraction from rock surface images,” Int. J. Rock Mech. Min. Sci, pp. 104732–104732, 2021.
Z. H. Kok, A. R. M. Shariff, M. S. M. Alfatni, and S. Khairunniza-Bejo, “Support Vector Machine in Precision Agriculture: A review,” Comput. Electron. Agric, pp. 106546–106546, 2021.
A. Salami and N. M, “Ant colony optimization algorithm,” UbiCC Journal, vol. 4, no. 3, pp. 823–826, 2009
P. W. Tsai, J. S. Pan, B. Y. Liao, and S. C. Chu, “Enhanced artificial bee colony optimization,” International Journal of Innovative Computing, Information and Control, vol. 5, no. 12, pp. 5081–5092, 2009.
X. S. Yang and X. He, “Firefly algorithm: recent advances and applications,” International journal of swarm intelligence, vol. 1, no. 1, pp. 36–50, 2013.
C. Zhang, F. M. Zhang, F. Li, and H. S. Wu, “Improved artificial fish swarm algorithm,” 9th IEEE conference on industrial electronics and applications, pp. 748–753, 2014.
S. Mirjalili and A. Lewis, “The whale optimization algorithm,” Advances in engineering software, vol. 95, pp. 51–67, 2016.
S. Saatchi and C. C. Hung, “Swarm intelligence and image segmentation. Swarm Intelligence,” Focus on Ant and Particle Swarm Optimization, vol. 1, pp. 163–178, 2007.
M. Boberek and K. Saeed, “Segmentation of MRI Brain Images for Automatic Detection and Precise Localization of Tumor,” in Image Processing and Communications Challenges 3. Advances in Intelligent and Soft Computing (C. R.S., ed.), vol. 102, Springer, 2011.
N. V. Shree, T. N. R. Kumar, and B. Inf, “Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network,” Brain informatics March, vol. 5, pp. 23–23, 2018.
A. Pinto, S. Pereira, H. Dinis, C. A. Silva, and D. M. L. D. Rasteiro, “Random decision forests for automatic brain tumor segmentation on multi-modal MRI images,” in 2015 IEEE 4th Portuguese Meeting on Bioengineering (ENBENG), pp. 1–5, 2015.
M. S. Atkins and B. T. Mackiewich, “Fully automatic segmentation of the brain in MRI,” IEEE Transactions on Medical Imaging, vol. 17, no. 1, pp. 98–107, 1998.
R. Jegadeesan, T. Karpagam, . N. Dr, and S. Ram Defending Wireless Network using Randomized Routing Process“ International journal of Emerging Research in management and Technology ISSN, pp. 2278–9359, 2014.
N. Abdullah, U. K. Ngah, and S. A. Aziz, “Image classification of brain MRI using support vector machine,” in 2011 IEEE International
Conference on Imaging Systems and Techniques, pp. 242–247, 2011.
“Improved Fuzzy C-Means based Particle Swarm Optimization (PSO) initialization and outlier rejection with level set methods for MR brain image segmentation “,AbdenourMekhmoukh ,KarimMokrani,” Computer Methods and Programs in Biomedicine, vol. 122, no. 2, pp. 266–281, 2015.
R. Jegadeesan and R. Sankar, “Defending Wireless Sensor Network using Randomized Routing,” International Journal of Advanced Research in Computer Science and Software Engineering, vol. 5, no. 9, pp. 2277–128, 2015.
C. Kaushal, M. K. Islam, S. A. Althubiti, F. Alenezi, and R. F. Mansour, “A framework for interactive medical image segmentation using optimized swarm intelligence with convolutional neural networks,” Computational intelligence and neuroscience, 2022.
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