Evaluation of Particle Swarm Optimisation for Medical Image Segmentation

Mohammad Hashem Ryalat; Daniel Emmens; Mark Hulse; Duncan Bell; Zainab Al-Rahamneh; Stephen Laycock; Mark Fisher

doi:10.1007/978-3-319-48944-5_6

Evaluation of Particle Swarm Optimisation for Medical Image Segmentation

Mohammad Hashem Ryalat, Daniel Emmens, Mark Hulse, Duncan Bell, Zainab Al-Rahamneh, Stephen Laycock, Mark Fisher

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

11 Citations (Scopus)

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.

Original language	English
Title of host publication	Advances in Systems Science
Subtitle of host publication	Proceedings of the International Conference on Systems Science 2016 (ICSS 2016)
Editors	Jerzy Świątek, Jakub M. Tomczak
Publisher	Springer
Pages	61-72
Number of pages	12
ISBN (Electronic)	978-3-319-48944-5
ISBN (Print)	978-3-319-48943-8
DOIs	https://doi.org/10.1007/978-3-319-48944-5_6
Publication status	Published - 5 Nov 2016

Publication series

Name	Advances in Intelligent Systems and Computing
Publisher	Springer International Publishing
Volume	539
ISSN (Print)	2194-5357
ISSN (Electronic)	2194-5365

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1007/978-3-319-48944-5_6

Stephen Laycock
- S.Laycockuea.acuk
- School of Computing Sciences - Professor of Computer Graphics
- Interactive Graphics and Audio - Member
Person: Research Group Member, Academic, Teaching & Research

Cite this

Ryalat, M. H., Emmens, D., Hulse, M., Bell, D., Al-Rahamneh, Z., Laycock, S., & Fisher, M. (2016). Evaluation of Particle Swarm Optimisation for Medical Image Segmentation. In J. Świątek, & J. M. Tomczak (Eds.), Advances in Systems Science: Proceedings of the International Conference on Systems Science 2016 (ICSS 2016) (pp. 61-72). [Chapter 6] (Advances in Intelligent Systems and Computing; Vol. 539). Springer. https://doi.org/10.1007/978-3-319-48944-5_6

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abstract = "Otsu{\textquoteright}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.",

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Ryalat, MH, Emmens, D, Hulse, M, Bell, D, Al-Rahamneh, Z, Laycock, S & Fisher, M 2016, Evaluation of Particle Swarm Optimisation for Medical Image Segmentation. in J Świątek & JM Tomczak (eds), Advances in Systems Science: Proceedings of the International Conference on Systems Science 2016 (ICSS 2016)., Chapter 6, Advances in Intelligent Systems and Computing, vol. 539, Springer, pp. 61-72. https://doi.org/10.1007/978-3-319-48944-5_6

Evaluation of Particle Swarm Optimisation for Medical Image Segmentation. / Ryalat, Mohammad Hashem; Emmens, Daniel; Hulse, Mark et al.

Advances in Systems Science: Proceedings of the International Conference on Systems Science 2016 (ICSS 2016). ed. / Jerzy Świątek; Jakub M. Tomczak. Springer, 2016. p. 61-72 Chapter 6 (Advances in Intelligent Systems and Computing; Vol. 539).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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T1 - Evaluation of Particle Swarm Optimisation for Medical Image Segmentation

AU - Ryalat, Mohammad Hashem

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AU - Al-Rahamneh, Zainab

AU - Laycock, Stephen

AU - Fisher, Mark

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AB - 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.

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Ryalat MH, Emmens D, Hulse M, Bell D, Al-Rahamneh Z, Laycock S et al. Evaluation of Particle Swarm Optimisation for Medical Image Segmentation. In Świątek J, Tomczak JM, editors, Advances in Systems Science: Proceedings of the International Conference on Systems Science 2016 (ICSS 2016). Springer. 2016. p. 61-72. Chapter 6. (Advances in Intelligent Systems and Computing). doi: 10.1007/978-3-319-48944-5_6

Evaluation of Particle Swarm Optimisation for Medical Image Segmentation

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Stephen Laycock

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