Investigation of Optimal Heuristical Parameters for Mixed ACO-k-means Segmentation Algorithm for MRI Images

DOI

10.2991/itsmssm-16.2016.72How to use a DOI?

Keywords

MRI image segmentation, ant colony optimization, k-means, swarm intelligence

Abstract

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.

Copyright

© 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/).

Download article (PDF)

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  - 10.2991/itsmssm-16.2016.72
ID  - El-Khatib2016/05
ER  -