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Breast lesion classification using ultrawideband early time breast lesion response

Breast lesion classification using ultrawideband early time breast lesion response

Teo, Jianqi, Chen, Yifan, Soh, Cheong Boon, Gunawan, E., Low, Kay Soon, Putti, T.C. and Wang, Shih-Chang (2010) Breast lesion classification using ultrawideband early time breast lesion response. IEEE Transactions on Antennas and Propagation, 58 (8). pp. 2604-2613. ISSN 0018 926X (doi:

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Breast lesion characterization for discriminating between localized malignant and benign lesions is important as the current breast screening techniques do not have the required specificity to be clinically acceptable. A method using ultrawideband (UWB) microwave imaging system for such classification is proposed in this paper. The early time portion of the backscatter breast response is processed for lesion discrimination. This method provides a high resolution since the early time lesion response has the largest signal strength. A correlator is used at the receiver to extract the early response and to quantify the degree of ruggedness of a lesion through several key parameters associated with the correlation operation. Subsequently, a large scale simulation study using a two-dimensional (2D) numerical breast model with an antenna array is used for the development of a lesion classification technique. It is shown that the lesion classification method is capable of discriminating between lesions with different morphologies.

Item Type: Article
Additional Information: Editor-in-Chief, Michael A. Jensen
Uncontrolled Keywords: breast lesion, localized malignant lesion, benign lesion, ultrawideband (UWB), backscatter breast response, simulation study, two-dimensional (2D) numerical breast model, antenna array
Subjects: R Medicine > R Medicine (General)
R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
R Medicine > RB Pathology
R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
Pre-2014 Departments: School of Engineering
Related URLs:
Last Modified: 14 Oct 2016 09:19
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None

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