Skip navigation

Deploying swarm intelligence in medical imaging identifying metastasis, micro-calcifications and brain image segmentation

Deploying swarm intelligence in medical imaging identifying metastasis, micro-calcifications and brain image segmentation

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)

[img]
Preview
PDF (Author's Accepted Manuscript)
21017 AL-RIFAIE_Deploying_Swarm_Intelligence_Medical_Imaging_(AAM)_2015.pdf - Accepted Version

Download (5MB) | Preview

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 / Department / Research Group: Faculty of Architecture, Computing & Humanities
Faculty of Architecture, Computing & Humanities > Centre for Computer & Computational Science
Faculty of Architecture, Computing & Humanities > Department of Computing & Information Systems
Last Modified: 12 Jul 2019 08:37
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
URI: http://gala.gre.ac.uk/id/eprint/21017

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics