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.
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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).
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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
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