Investigation of Optimal Heuristical Parameters for Mixed ACO-k-means Segmentation Algorithm for MRI Images
- https://doi.org/10.2991/itsmssm-16.2016.72How to use a DOI?
- MRI image segmentation, ant colony optimization, k-means, swarm intelligence
The parameters of the modified mixed Ant Colony Optimization (ACO) - k-means image segmentation algorithm are considered. There have been investigated such parameters as n - the number of ants; heuristic coefficients of ACO algorithm and their dependence on the image scale and number of iterations before and after parameters correction. The proposed algorithm and sub-system for the study of coefficients, as part of the medical image segmentation system, have been implemented. Operation of the algorithm with and without the use of optimal parameters was applied. Optimal parameters were studied for 6 groups of MRI images: brain, heart, lungs, liver, bone structures, and others. The results are displayed in the final table. Images from Ossirix image dataset and real patients' images were used for testing.
- © 2016, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Samer El-Khatib AU - Sergey Rodzin AU - Yuri Skobtsov PY - 2016/05 DA - 2016/05 TI - Investigation of Optimal Heuristical Parameters for Mixed ACO-k-means Segmentation Algorithm for MRI Images BT - Proceedings of the 2016 Conference on Information Technologies in Science, Management, Social Sphere and Medicine PB - Atlantis Press SP - 355 EP - 360 SN - 2352-538X UR - https://doi.org/10.2991/itsmssm-16.2016.72 DO - https://doi.org/10.2991/itsmssm-16.2016.72 ID - El-Khatib2016/05 ER -