Abstract
Image segmentation is the process of subdividing an image into regions that are consistent and homogeneous in some characteristics. An important factor in the recognition of magnetic resonance images is not only the accuracy, but also the speed of the segmentation procedure. Modified Exponential Particle Swarm Optimization algorithm is proposed in paper. The time complexity of proposed algorithm is investigated using consequences from Drift theorem. It is established that the proposed algorithm has a polynomial estimation of complexity. Images from the Ossirix image dataset and real medical images were used for testing.
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References
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Englewood Cliffs (2008)
Kennedy, J, Eberhart, R.C.: Particle swarm intelligence. In: Proceedings of the IEEE International Joint Conference on Neural Networks, pp. 1942–1948 (1995)
El-Khatib, S., Rodzin, S., Skobtcov, Y.: Investigation of optimal heuristical parameters for mixed ACO-k-means segmentation algorithm for MRI images. In: Proceedings of III International Scientific Conference on Information Technologies in Science, Management, Social Sphere and Medicine (ITSMSSM 2016). Part of series Advances in Computer Science Research, vol. 51 pp. 216–221. Atlantis Press (2016). https://doi.org/10.2991/itsmssm-16.2016.72. ISBN: 978-94-6252-196-4
El-Khatib, S., Skobtsov, Y., Rodzin, S.: Theoretical and experimental evaluation of hybrid ACO-k-means image segmentation algorithm for MRI images using drift-analysis. In: Proceedings of XIII International Symposium « ntelligent Systems – 2018» (INTELS 2018), St. Petersburg, Russia, 22–24 Oct 2018, pp. 1–9. Procedia Computer Science
El-Khatib, S.: Modified exponential particle swarm optimization algorithm for medical image segmentation. In: Proceedings of XIX International Conference on Soft Computing and Measurements (SCM 2016), St. Petersburg, 25–27 May 2016, vol. 1, pp. 513–516
Saatchi, S., Hung, C.C.: Swarm intelligence and image segmentation swarm intelligence. ARS J (2007)
Das, S., Abraham, A., Konar, A.: Automatic kernel clustering with a multi-elitist particle swarm optimization algorithm. Pattern Recogn. Lett. 29, 688–699 (2008). Science direct
El-Khatib, S., Skobtsov, Y., Rodzin, S., Zelentsov, V.: Hyper-heuristical particle swarm method for MR images segmentation. In: Silhavy, R. (eds) Artificial Intelligence and Algorithms in Intelligent Systems. CSOC2018. Advances in Intelligent Systems and Computing, vol. 764, pp. 256–264. Springer, Berlin (2018)
Skobtsov, Y.A., Speransky, D.V.: Evolutionary computation: hand book. The National Open University “INTUIT”, Moscow, 331 p. (2015). (in Russian)
He, J., Yao, X.: Drift analysis and average time complexity of evolutionary algorithms. In.: Artificial Intelligence, vol. 127, no. 1, pp. 57–85 (2001)
Acknowledgements
The research described in this paper is partially supported by the Russian Foundation for Basic Research according to the research project № 19-07-00570 “Bio-inspired models of problem-oriented systems and methods of their application for clustering, classification, filtering and optimization problems, including big data” (Southern Federal University).
The research described in this paper is partially supported by the Russian Foundation for Basic Research (grants 16-29-09482-ofi-i, 17-08-00797, 17-06-00108, 17-01-00139, 17-20-01214, 17-29-07073-ofi-i, 18-07-01272, 18-08-01505, 19-08-00989) state order of the Ministry of Education and Science of the Russian Federation № 2.3135.2017/4.6, state research 0073–2019–0004, International project ERASMUS +, Capacity building in higher education, № 73751-EPP-1-2016-1-DE-EPPKA2-CBHE-JP, Innovative teaching and learning strategies in open modelling and simulation environment for student-centered engineering education.
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El-Khatib, S., Skobtsov, Y., Rodzin, S., Potryasaev, S. (2019). Theoretical and Experimental Evaluation of PSO-K-Means Algorithm for MRI Images Segmentation Using Drift Theorem. In: Silhavy, R. (eds) Artificial Intelligence Methods in Intelligent Algorithms. CSOC 2019. Advances in Intelligent Systems and Computing, vol 985. Springer, Cham. https://doi.org/10.1007/978-3-030-19810-7_31
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DOI: https://doi.org/10.1007/978-3-030-19810-7_31
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