Hyper-heuristical particle swarm method for MR images segmentation

El-Khatib S., Skobtsov Y. Rodzin S, Zelentsov V. Hyper-heuristical particle swarm method for MR images segmentation // Advances in Intelligent Systems and Computing, Volume 764, 2019. 7th Computer Science On-line Conference, CSOC 2018; Zlin; Czech Republic; 25 April 2018 to 28 April 2018. P.256–264. http://doi.org/10.1007/978-3-319-91189-2_25


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.