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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...

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Autores principales: Dey, Maitreyee, Rana, Soumya Prakash, Loretoni, Riccardo, Duranti, Michele, Sani, Lorenzo, Vispa, Alessandro, Raspa, Giovanni, Ghavami, Mohammad, Dudley, Sandra, Tiberi, Gianluigi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
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.
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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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