Comparison of Hybrid ACO-k-means algorithm and Grub cut for MRI images segmentation

• Published in: Procedia computer science Vol. 186; pp. 316 - 322
• Main Authors: El-Khatib, S.A., Skobtsov, Y.A., Rodzin, S.I.
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 2021
• Subjects: ant colony optimization; grub cut; k-means algorithm; MRI image segmentation; swarm intelligence; grub cut; ant colony optimization; k-means algorithm; swarm intelligence; MRI image segmentation
• ISSN: 1877-0509; 1877-0509
• DOI: 10.1016/j.procs.2021.04.150

Image segmentation is the process of dividing image into homogenous regions by some charasteristics and is widely used in medical diagnostics. Segmentation algorithms are used for anatomical features extraction from medical images. The Hybrid Ant Colony Optimization (ACO) – k-means and Grub Cut image segmentation algorithms for MRI images segmentation are considered in this paper. The proposed algorithms and sub-system for the medical image segmentation have been implemented. As there is no universal algorithm for medical image segmentation, image segmentation is still a challenging problem in image processing and computer vision in many real time applications and hence more research work is required. The experimental results show that the proposed algorithm has good accuracy in comparison to Grub cut.