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Multiple-input multiple-output radar for lesion classification in ultrawideband breast imaging

Multiple-input multiple-output radar for lesion classification in ultrawideband breast imaging

Chen, Yifan, Craddock, I.J., Kosmas, P., Ghavami, M. and Rapajic, P. (2010) Multiple-input multiple-output radar for lesion classification in ultrawideband breast imaging. IEEE Journal of Selected Topics in Signal Processing, 4 (1). pp. 187-201. ISSN 1932-4553 (doi:10.1109/JSTSP.2009.2038975)

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Abstract

This paper studies the problem of applying multiple-input multiple-output (MIMO) radar techniques for lesion classification in ultrawideband (UWB) breast imaging. Ongoing work on this topic has suggested that benign and malignant masses, which usually possess remarkable architectural differences, could be distinguished by exploiting their morphology-dependent UWB microwave backscatter. We have previously approached this problem by deriving the complex natural resonances of the late-time target response, where the damping factors vary with the border profiles of anomalies. In this paper, we investigate the potential advantage of MIMO radar to enhance the resonance scattering phenomenon in breast tissue discrimination. MIMO radar can choose freely the probing signals transmitted via its antennas to exploit the independence between signals at the array elements, thereby enhancing the performance of target classification. Based on the observed damping factors and the receiver operating characteristics at different classifiers, which correspond to various diversity paths in the MIMO radar system, two data-fusion rules are proposed for robust lesion differentiation. Finally, numerical examples are provided to demonstrate the efficacy of the proposed imaging technique.

Item Type: Article
Additional Information: Editor-in-Chief, Vikram Krishnamurthy
Uncontrolled Keywords: multiple-input multiple-output (MIMO), ultrawideband (UWB), breast imaging, morphology-dependent UWB microwave backscatter, MIMO radar, data-fusion rules
Subjects: R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer)
T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Pre-2014 Departments: School of Engineering
School of Engineering > Department of Computer & Communications 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
URI: http://gala.gre.ac.uk/id/eprint/7768

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