Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network
Journal article
Dey, M., Rana, S., Loretoni, R., Duranti, M., Sani, L., Vispa, A., Raspa, G., Ghavami, M., Dudley-Mcevoy, S. and Tiberi, G. (2022). Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network. PLoS ONE. https://doi.org/10.1371/journal.pone.0271377
Authors | Dey, M., Rana, S., Loretoni, R., Duranti, M., Sani, L., Vispa, A., Raspa, G., Ghavami, M., Dudley-Mcevoy, S. and Tiberi, G. |
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Abstract | 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). The proposed method is tested on microwave images of 61 breasts acquired from a feasibility study performed in Foligno Hospital, Italy. Subsequently, a non-parametric thresholding technique is modelled to differentiate between breasts having no radiological finding (NF) or benign |
Keywords | MammoWave, Pulse coupled neural network, Image segmentation, Breast lesion detection |
Year | 2022 |
Journal | PLoS ONE |
Publisher | Public Library of Science (PLoS) |
ISSN | 1932-6203 |
Digital Object Identifier (DOI) | https://doi.org/10.1371/journal.pone.0271377 |
Publication dates | |
21 Jul 2022 | |
Publication process dates | |
Accepted | 29 Jun 2022 |
Deposited | 15 Jul 2022 |
Publisher's version | License File Access Level Open |
Accepted author manuscript | License File Access Level Controlled |
https://openresearch.lsbu.ac.uk/item/915vx
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