Abstract
An important factor in the recognition of magnetic resonance images is not only the accuracy, but also the speed of the segmentation procedure. In some cases, the speed of the procedure is more important than the accuracy and the choice is made in favor of a less accurate, but faster procedure. This means that the segmentation method must be fully adaptive to different image models, that reduces its accuracy. These requirements are satisfied by developed hyper-heuristical particle swarm method for image segmentation. The main idea of the proposed hyper-heuristical method is the application of several heuristics, each of which has its strengths and weaknesses, and then their use depending on the current state of the solution. Hyper-heuristical particle swarm segmentation method is a management system, in the subordination of which there are three bioinspired heuristics: PSO-K-means, Modified Exponential PSO, Elitist Exponential PSO. Developed hyper-heuristical method was tested using the Ossirix benchmark with magnetic-resonance images (MRI) with various nature and different quality. The results of method’s work and a comparison with competing segmentation methods are presented in the form of an accuracy chart and a time table of segmentation methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Upper Saddle River (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. Published by Atlantis Press (2016). https://doi.org/10.2991/itsmssm-16.2016.72. ISBN (on-line): 978-94-6252-196-4
El-Khatib, S.A., Skobtcov, Y.A.: System of medical image segmentation using ant colony optimization. St. Petersburg State Polytech. Univ. J. Comput. Sci. Telecommun. Control Syst. 2(217)–3(222), 9–19 (2015). https://doi.org/10.5862/jcstcs/1
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 (2016)
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, science direct. Pattern Recogn. Lett. 29, 688–699 (2008)
Ossirix image dataset. http://www.osirix-viewer.com/
Ghamisi, P., Couceiro, M.S., Ferreira, M.F., Kumar, L.: An efficient method for segmentation of remote sensing images based on darwinian particle swarm optimization. In: Proceedings of the IEEE International Geoscience and Remote Sensing Symposium – Remote Sensing for a Dynamic Earth (IGARSS 2012), pp. 20–28 (2012)
Ghamisi, P., Couceiro, M.S., Martins, M.L., Benediktsson, J.A.: Multilevel image segmentation based on fractional-order darwinian particle swarm optimization. IEEE Trans. Geosci. Remote Sens. 52(5), 1–13 (2013)
Skobtsov, Y.A., Speransky, D.V.: Evolutionary Computation: Hand Book, 331 p. The National Open University “INTUIT”, Moscow (2015). (in Russian)
Acknowledgements
The research on creating the hyper-heuristic method for image segmentation was supported by the Russian Science Foundation (project 17-11-01254).
The research described in Section 2.3 of this article is partially supported by the state research 0073–2018–0003. Approbation of the results is partially supported by the Russian Science Foundation (project 16-07-00336).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
El-Khatib, S., Skobtsov, Y., Rodzin, S., Zelentsov, V. (2019). Hyper-heuristical Particle Swarm Method for MR Images Segmentation. In: Silhavy, R. (eds) Artificial Intelligence and Algorithms in Intelligent Systems. CSOC2018 2018. Advances in Intelligent Systems and Computing, vol 764. Springer, Cham. https://doi.org/10.1007/978-3-319-91189-2_25
Download citation
DOI: https://doi.org/10.1007/978-3-319-91189-2_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91188-5
Online ISBN: 978-3-319-91189-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)