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Body Area Networks. Smart IoT and Big Data for Intelligent Health. 15th EAI International Conference, BODYNETS 2020, Tallinn, Estonia, October 21, 2020, Proceedings

Research Article

UWB Microwave Imaging for Inclusions Detection: Methodology for Comparing Artefact Removal Algorithms

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  • @INPROCEEDINGS{10.1007/978-3-030-64991-3_4,
        author={James Puttock and Behnaz Sohani and Banafsheh Khalesi and Gianluigi Tiberi and Sandra Dudley-McEvoy and Mohammad Ghavami},
        title={UWB Microwave Imaging for Inclusions Detection: Methodology for Comparing Artefact Removal Algorithms},
        proceedings={Body Area Networks. Smart IoT and Big Data for Intelligent Health. 15th EAI International Conference, BODYNETS 2020, Tallinn, Estonia, October 21, 2020, Proceedings},
        proceedings_a={BODYNETS},
        year={2020},
        month={12},
        keywords={UWB microwave imaging Image quality metric Artefact removal},
        doi={10.1007/978-3-030-64991-3_4}
    }
    
  • James Puttock
    Behnaz Sohani
    Banafsheh Khalesi
    Gianluigi Tiberi
    Sandra Dudley-McEvoy
    Mohammad Ghavami
    Year: 2020
    UWB Microwave Imaging for Inclusions Detection: Methodology for Comparing Artefact Removal Algorithms
    BODYNETS
    Springer
    DOI: 10.1007/978-3-030-64991-3_4
James Puttock1,*, Behnaz Sohani1, Banafsheh Khalesi1, Gianluigi Tiberi1, Sandra Dudley-McEvoy1, Mohammad Ghavami1
  • 1: Department of Electrical and Electronic Engineering
*Contact email: puttockj@lsbu.ac.uk

Abstract

An investigation is presented on Artefact Removal Methods for Ultra-Wideband (UWB) Microwave Imaging. Simulations have been done representing UWB signals transmitted onto a cylindrical head-mimicking phantom containing an inclusion having dielectric properties imitating an haemorrhagic stroke. The ideal image is constructed by applying a Huygens’ Principle based imaging algorithm to the difference between the electric field outside the cylinder with an inclusion and the electric field outside the same cylinder with no inclusion. Eight different artefact removal methods are then applied, with the inclusion positioned at( \pi )and( - \frac{\pi }{4} )radians, respectively. The ideal image is then used as a reference image to compare the artefact removal methods employing a novel Image Quality Index, calculated using a weighted combination of image quality metrics. The Summed Symmetric Differential method performed very well in our simulations.

Keywords
UWB microwave imaging Image quality metric Artefact removal
Published
2020-12-15
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-64991-3_4
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