Al-Rifaie, Mohammad Majid ORCID: 0000-0002-1798-9615, Aber, Ahmed and Hemanth, Duraiswamy Jude (2015) Deploying swarm intelligence in medical imaging identifying metastasis, micro-calcifications and brain image segmentation. IET Systems Biology, 9 (6). pp. 234-244. ISSN 1751-8849 (Print), 1751-8857 (Online) (doi:https://doi.org/10.1049/iet-syb.2015.0036)

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Abstract

This paper proposes an umbrella deployment of swarm intelligence algorithm such as Stochastic Diffusion Search for medical imaging applications. After summarising the results of some previous work which shows how the algorithm assists in the identification of metastasis in bone scans and microcalcifications on mammographs, for the first time, the use of the algorithm in assessing the CT images of aorta is demonstrated along with its performance in detecting the nasogastric tube in chest X-ray. The swarm intelligence algorithm presented in this paper is adapted to address these particular tasks and its functionality is investigated by running the swarms on sample CT images and X-rays whose status have been determined by senior radiologists. Additionally, a hybrid swarm intelligence-Learning Vector Quantisation (LVQ) approach is proposed in the context of Magnetic Resonance (MR) brain image segmentation. The Particle Swarm Optimisation (PSO) is used to train the LVQ which eliminates the iteration- dependent nature of LVQ. The proposed methodology is used to detect the tumour regions in the abnormal MR brain images.

Item Type:	Article
Uncontrolled Keywords:	swarm intelligence, medical imaging, metastasis, micro-calcifications, brain image segmentation, CT scan, stochastic diffusion search, particle swarm optimisation
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / School / Research Centre / Research Group:	Faculty of Liberal Arts & Sciences > Computational Science & Engineering Group (CSEH) Faculty of Engineering & Science > School of Computing & Mathematical Sciences (CMS) Faculty of Engineering & Science
Last Modified:	04 Mar 2022 13:07
URI:	http://gala.gre.ac.uk/id/eprint/21017

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