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Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network
MammoWave is a microwave imaging device for breast lesion detection, employing two antennas which rotate azimuthally (horizontally) around the breast. The antennas operate in the 1-9 GHz band and are set in free space, i.e., pivotally, no matching liquid is required. Microwave images, subsequently o...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9302781/ https://www.ncbi.nlm.nih.gov/pubmed/35862368 http://dx.doi.org/10.1371/journal.pone.0271377 |
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author | Dey, Maitreyee Rana, Soumya Prakash Loretoni, Riccardo Duranti, Michele Sani, Lorenzo Vispa, Alessandro Raspa, Giovanni Ghavami, Mohammad Dudley, Sandra Tiberi, Gianluigi |
author_facet | Dey, Maitreyee Rana, Soumya Prakash Loretoni, Riccardo Duranti, Michele Sani, Lorenzo Vispa, Alessandro Raspa, Giovanni Ghavami, Mohammad Dudley, Sandra Tiberi, Gianluigi |
author_sort | Dey, Maitreyee |
collection | PubMed |
description | MammoWave is a microwave imaging device for breast lesion detection, employing two antennas which rotate azimuthally (horizontally) around the breast. The antennas operate in the 1-9 GHz band and are set in free space, i.e., pivotally, no matching liquid is required. Microwave images, subsequently obtained through the application of Huygens Principle, are intensity maps, representing the homogeneity of the dielectric properties of the breast tissues under test. In this paper, MammoWave is used to realise tissues dielectric differences and localise lesions by segmenting microwave images adaptively employing pulse coupled neural network (PCNN). Subsequently, a non-parametric thresholding technique is modelled to differentiate between breasts having no radiological finding (NF) or benign (BF) and breasts with malignant finding (MF). Resultant findings verify that automated breast lesion localization with microwave imaging matches the gold standard achieving 81.82% sensitivity in MF detection. The proposed method is tested on microwave images acquired from a feasibility study performed in Foligno Hospital, Italy. This study is based on 61 breasts from 35 patients; performance may vary with larger number of datasets and will be subsequently investigated. |
format | Online Article Text |
id | pubmed-9302781 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-93027812022-07-22 Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network Dey, Maitreyee Rana, Soumya Prakash Loretoni, Riccardo Duranti, Michele Sani, Lorenzo Vispa, Alessandro Raspa, Giovanni Ghavami, Mohammad Dudley, Sandra Tiberi, Gianluigi PLoS One Research Article MammoWave is a microwave imaging device for breast lesion detection, employing two antennas which rotate azimuthally (horizontally) around the breast. The antennas operate in the 1-9 GHz band and are set in free space, i.e., pivotally, no matching liquid is required. Microwave images, subsequently obtained through the application of Huygens Principle, are intensity maps, representing the homogeneity of the dielectric properties of the breast tissues under test. In this paper, MammoWave is used to realise tissues dielectric differences and localise lesions by segmenting microwave images adaptively employing pulse coupled neural network (PCNN). Subsequently, a non-parametric thresholding technique is modelled to differentiate between breasts having no radiological finding (NF) or benign (BF) and breasts with malignant finding (MF). Resultant findings verify that automated breast lesion localization with microwave imaging matches the gold standard achieving 81.82% sensitivity in MF detection. The proposed method is tested on microwave images acquired from a feasibility study performed in Foligno Hospital, Italy. This study is based on 61 breasts from 35 patients; performance may vary with larger number of datasets and will be subsequently investigated. Public Library of Science 2022-07-21 /pmc/articles/PMC9302781/ /pubmed/35862368 http://dx.doi.org/10.1371/journal.pone.0271377 Text en © 2022 Dey et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Dey, Maitreyee Rana, Soumya Prakash Loretoni, Riccardo Duranti, Michele Sani, Lorenzo Vispa, Alessandro Raspa, Giovanni Ghavami, Mohammad Dudley, Sandra Tiberi, Gianluigi Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network |
title | Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network |
title_full | Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network |
title_fullStr | Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network |
title_full_unstemmed | Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network |
title_short | Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network |
title_sort | automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9302781/ https://www.ncbi.nlm.nih.gov/pubmed/35862368 http://dx.doi.org/10.1371/journal.pone.0271377 |
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