A fast and automatic approach for removing artefacts due to immobilisation masks in X-ray CT

Mohammad Hashem Ryalat; Stephen Laycock; Mark Fisher

doi:10.1109/BHI.2017.7897198

A fast and automatic approach for removing artefacts due to immobilisation masks in X-ray CT

Mohammad Hashem Ryalat, Stephen Laycock, Mark Fisher

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

2 Citations (Scopus)

14 Downloads (Pure)

Abstract

Immobilisation masks are fixation devices that are used when administering radiotherapy treatment to patients with tumours affecting the head and neck. Radiotherapy planning X-ray Computer Tomography (CT) data sets for these patients are captured with the immobilisation mask fitted and manually editing the X-ray CT images to remove artefacts due to the mask is time consuming and error prone. This paper represents the first study that employs a fast and automatic approach to remove image artefacts due to masks in X-ray CT images
without affecting pixel values representing tissue. Our algorithm uses a fractional order Darwinian particle swarm optimisation of Otsu’s method combined with morphological post-processing to classify pixels belonging to the mask. The proposed approach is tested on five X-ray CT data sets and achieves an average specificity of 92.01% and sensitivity of 99.39%. We also present results demonstrating the comparative speed-up obtained by fractional order Darwinian particle swarm optimisation.

Original language	English
Title of host publication	2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)
Publisher	The Institute of Electrical and Electronics Engineers (IEEE)
DOIs	https://doi.org/10.1109/BHI.2017.7897198
Publication status	Published - 13 Apr 2017
Event	2017 IEEE International Conference on Biomedical and Health Informatics - Orlanda, United States Duration: 16 Feb 2017 → 19 Feb 2017

Conference

Conference	2017 IEEE International Conference on Biomedical and Health Informatics
Country/Territory	United States
City	Orlanda
Period	16/02/17 → 19/02/17

Keywords

Immobilisation Mask
CT Images
Head and Neck Cancer

UN SDGs

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

Access to Document

10.1109/BHI.2017.7897198

Accepted manuscriptAccepted author manuscript, 1.04 MB

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

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title = "A fast and automatic approach for removing artefacts due to immobilisation masks in X-ray CT",

abstract = "Immobilisation masks are fixation devices that are used when administering radiotherapy treatment to patients with tumours affecting the head and neck. Radiotherapy planning X-ray Computer Tomography (CT) data sets for these patients are captured with the immobilisation mask fitted and manually editing the X-ray CT images to remove artefacts due to the mask is time consuming and error prone. This paper represents the first study that employs a fast and automatic approach to remove image artefacts due to masks in X-ray CT imageswithout affecting pixel values representing tissue. Our algorithm uses a fractional order Darwinian particle swarm optimisation of Otsu{\textquoteright}s method combined with morphological post-processing to classify pixels belonging to the mask. The proposed approach is tested on five X-ray CT data sets and achieves an average specificity of 92.01% and sensitivity of 99.39%. We also present results demonstrating the comparative speed-up obtained by fractional order Darwinian particle swarm optimisation.",

keywords = "Immobilisation Mask, CT Images, Head and Neck Cancer",

author = "Ryalat, {Mohammad Hashem} and Stephen Laycock and Mark Fisher",

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doi = "10.1109/BHI.2017.7897198",

language = "English",

booktitle = "2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)",

publisher = "The Institute of Electrical and Electronics Engineers (IEEE)",

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Ryalat, MH, Laycock, S & Fisher, M 2017, A fast and automatic approach for removing artefacts due to immobilisation masks in X-ray CT. in 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). The Institute of Electrical and Electronics Engineers (IEEE), 2017 IEEE International Conference on Biomedical and Health Informatics, Orlanda, Florida, United States, 16/02/17. https://doi.org/10.1109/BHI.2017.7897198

A fast and automatic approach for removing artefacts due to immobilisation masks in X-ray CT. / Ryalat, Mohammad Hashem; Laycock, Stephen ; Fisher, Mark.

2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). The Institute of Electrical and Electronics Engineers (IEEE), 2017.

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

TY - GEN

T1 - A fast and automatic approach for removing artefacts due to immobilisation masks in X-ray CT

AU - Ryalat, Mohammad Hashem

AU - Laycock, Stephen

AU - Fisher, Mark

PY - 2017/4/13

Y1 - 2017/4/13

N2 - Immobilisation masks are fixation devices that are used when administering radiotherapy treatment to patients with tumours affecting the head and neck. Radiotherapy planning X-ray Computer Tomography (CT) data sets for these patients are captured with the immobilisation mask fitted and manually editing the X-ray CT images to remove artefacts due to the mask is time consuming and error prone. This paper represents the first study that employs a fast and automatic approach to remove image artefacts due to masks in X-ray CT imageswithout affecting pixel values representing tissue. Our algorithm uses a fractional order Darwinian particle swarm optimisation of Otsu’s method combined with morphological post-processing to classify pixels belonging to the mask. The proposed approach is tested on five X-ray CT data sets and achieves an average specificity of 92.01% and sensitivity of 99.39%. We also present results demonstrating the comparative speed-up obtained by fractional order Darwinian particle swarm optimisation.

AB - Immobilisation masks are fixation devices that are used when administering radiotherapy treatment to patients with tumours affecting the head and neck. Radiotherapy planning X-ray Computer Tomography (CT) data sets for these patients are captured with the immobilisation mask fitted and manually editing the X-ray CT images to remove artefacts due to the mask is time consuming and error prone. This paper represents the first study that employs a fast and automatic approach to remove image artefacts due to masks in X-ray CT imageswithout affecting pixel values representing tissue. Our algorithm uses a fractional order Darwinian particle swarm optimisation of Otsu’s method combined with morphological post-processing to classify pixels belonging to the mask. The proposed approach is tested on five X-ray CT data sets and achieves an average specificity of 92.01% and sensitivity of 99.39%. We also present results demonstrating the comparative speed-up obtained by fractional order Darwinian particle swarm optimisation.

KW - Immobilisation Mask

KW - CT Images

KW - Head and Neck Cancer

U2 - 10.1109/BHI.2017.7897198

DO - 10.1109/BHI.2017.7897198

M3 - Conference contribution

BT - 2017 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI)

PB - The Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2017 IEEE International Conference on Biomedical and Health Informatics

Y2 - 16 February 2017 through 19 February 2017

ER -

A fast and automatic approach for removing artefacts due to immobilisation masks in X-ray CT

Abstract

Conference

Keywords

UN SDGs

Access to Document

Profiles

Stephen Laycock

Cite this