Evaluation of Particle Swarm Optimisation for Medical Image Segmentation

Ryalat, Mohammad Hashem, Emmens, Daniel, Hulse, Mark, Bell, Duncan, Al-Rahamneh, Zainab, Laycock, Stephen and Fisher, Mark (2016) Evaluation of Particle Swarm Optimisation for Medical Image Segmentation. In: Advances in Systems Science. Advances in Intelligent Systems and Computing . Springer, pp. 61-72. ISBN 978-3-319-48943-8

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Abstract

Otsu’s criteria is a popular image segmentation approach that selects a threshold to maximise the inter-class variance of the distribution of intensity levels in the image. The algorithm finds the optimum threshold by performing an exhaustive search, but this is time-consuming, particularly for medical images employing 16-bit quantisation. This paper investigates particle swarm optimisation (PSO), Darwinian PSO and Fractional Order Darwinian PSO to speed up the algorithm. We evaluate the algorithms in medical imaging applications concerned with volume reconstruction, with a particular focus on addressing artefacts due to immobilisation masks, commonly worn by patients undergoing radiotherapy treatment for head-and-neck cancer. We find that the Fractional-Order Darwinian PSO algorithm outperforms other PSO algorithms in terms of accuracy, stability and speed which makes it the favourite choice when the accuracy and time-of-execution are a concern.

Item Type: Book Section
Faculty \ School: Faculty of Science > School of Computing Sciences
Faculty of Science
Depositing User: Pure Connector
Date Deposited: 10 Nov 2016 17:00
Last Modified: 09 Aug 2019 13:31
URI: https://ueaeprints.uea.ac.uk/id/eprint/61307
DOI: 10.1007/978-3-319-48944-5_6

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