TY - JOUR
T1 - Automated breast lesion localisation in microwave imaging employing simplified pulse coupled neural network
AU - Dey, Maitreyee
AU - Rana, Soumya
AU - Ghavami, Mohammad
AU - Dudley-mcevoy, Sandra
AU - Tiberi, Gianluigi
PY - 2022/7/21
Y1 - 2022/7/21
N2 - 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
(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. This study is based on 61 breasts initially, performance may vary with larger number of datasets and this will be investigated.
AB - 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
(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. This study is based on 61 breasts initially, performance may vary with larger number of datasets and this will be investigated.
KW - MammoWave, Pulse coupled neural network, Image segmentation, Breast lesion detection
U2 - 10.1371/journal.pone.0271377
DO - 10.1371/journal.pone.0271377
M3 - Article
SN - 1932-6203
VL - 17
JO - PLoS ONE
JF - PLoS ONE
IS - 7 July
M1 - e0271377
ER -