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dc.contributor.advisorJones, Edward
dc.contributor.advisorGlavin, Martin
dc.contributor.authorByrne, Dallan
dc.date.accessioned2012-11-07T12:50:25Z
dc.date.available2012-11-07T12:50:25Z
dc.date.issued2012-01-18
dc.identifier.urihttp://hdl.handle.net/10379/3035
dc.description.abstractUltrawideband (UWB) microwave imaging is an emerging breast screening modality based on the electromagnetic reflections generated by the dielectric contrast between soft tissue types within the breast at microwave frequencies, most notably between malignant and fatty tissue. However, the breast can contain significant amounts of fibroglandular or fibroconnective tissues, which reduce the dielectric contrast between healthy and cancerous tissue and limits the effectiveness of UWB imaging algorithms. This dissertation focuses on UWB scanning techniques to detect cancer, with a par- ticular focus on the dielectrically heterogeneous breast. The work can be viewed in two parts: UWB breast cancer imaging and UWB breast cancer detection. The first part focuses on UWB beamforming algorithms and their ability to image a tumour response when the breast contains significant levels of glandular tissue. A number of Data Independent beamforming methods are compared using metrics, where signal data are generated from 3D Finite-Difference Time-Domain (FDTD) models adapted from MRI-derived phantoms. A novel extension of a Data Adap- tive imaging algorithm is presented and is shown to significantly outperform existing beamformers, particularly in a dielectrically heterogeneous breast. The application of contrast agents with UWB imaging methods is shown to successfully image non- palpable tumours when significant levels of fibroglandular tissue are present. The second area of study focuses on using a Support Vector Machine (SVM) based UWB cancer detection system to identify tumour backscatter within the received UWB signal data. The algorithm is extended to process the temporal changes between signals using an SVM and is shown to successfully detect non-palpable tumours when fluctuations in glandular tissue density and cancerous growth can occur during the time between scans.en_US
dc.subjectUltrawidebanden_US
dc.subjectBreast imagingen_US
dc.subjectBeamformingen_US
dc.subjectElectrical and Electronic Engineeringen_US
dc.subjectEngineering and Informaticsen_US
dc.titleUltrawideband Radar for the Early Detection of Cancer Within the Heterogeneous Breasten_US
dc.typeThesisen_US
dc.contributor.funderIrcset Enterprise Partnership Schemeen_US
dc.contributor.funderHewlett Packarden_US
dc.local.noteUltrawideband (UWB) microwave imaging is an emerging breast screening modality based on the electromagnetic reflections generated by the dielectric contrast between soft tissue types within the breast at microwave frequencies, most notably between malignant and fatty tissue. However, the breast can contain significant amounts of fibroglandular or fibroconnective tissues, which reduce the dielectric contrast between healthy and cancerous tissue and limits the effectiveness of UWB imaging algorithms. This dissertation focuses on UWB scanning techniques to detect cancer, with a par- ticular focus on the dielectrically heterogeneous breast. The work can be viewed in two parts: UWB breast cancer imaging and UWB breast cancer detection. The first part focuses on UWB beamforming algorithms and their ability to im- age a tumour response when the breast contains significant levels of glandular tissue. A number of Data Independent beamforming methods are compared using metrics, where signal data are generated from 3D Finite-Difference Time-Domain (FDTD) models adapted from MRI-derived phantoms. A novel extension of a Data Adap- tive imaging algorithm is presented and is shown to significantly outperform existing beamformers, particularly in a dielectrically heterogeneous breast. The application of contrast agents with UWB imaging methods is shown to successfully image non- palpable tumours when significant levels of fibroglandular tissue are present. The second area of study focuses on using a Support Vector Machine (SVM) based UWB cancer detection system to identify tumour backscatter within the received UWB signal data. The algorithm is extended to process the temporal changes between signals using an SVM and is shown to successfully detect non-palpable tumours when fluctuations in glandular tissue density and cancerous growth can occur during the time between scans.en_US
dc.local.finalYesen_US
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