A Bayesian nonparametric approach to tumor detection using UWB imaging
We develop a tumor detection and discrimination algorithm for Ultra-Wideband (UWB) microwave imaging of breast cancer based on a Bayesian nonparametric approach. We model the UWB backscattered signal as a mixture of distinct scatterer contributions, and use a Dirichlet Process mixture model (DPMM) t...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2013
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| Online Access: | https://hdl.handle.net/10356/99490 http://hdl.handle.net/10220/12892 |
| _version_ | 1826122583279403008 |
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| author | Nijsure, Yogesh Tay, Wee Peng Gunawan, Erry Yue, Joshua Lai Chong |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Nijsure, Yogesh Tay, Wee Peng Gunawan, Erry Yue, Joshua Lai Chong |
| author_sort | Nijsure, Yogesh |
| collection | NTU |
| description | We develop a tumor detection and discrimination algorithm for Ultra-Wideband (UWB) microwave imaging of breast cancer based on a Bayesian nonparametric approach. We model the UWB backscattered signal as a mixture of distinct scatterer contributions, and use a Dirichlet Process mixture model (DPMM) to describe the amplitudes and delays of the backscattered returns. Because of the unbounded complexity afforded by the DPMM, model under-fitting is avoided and parameters like the clutter covariance matrix in other commonly used approaches, need not be estimated. The DPMM allows us to perform discrimination when there are multiple tumor and clutter sources that present as extended radar targets. After performing discrimination, we distinguish the tumor sources from other clutter sources using a generalized likelihood ratio test (GLRT). We perform experiments on a breast phantom with realistic dielectric contrast ratios, and compare the performance of our algorithm with a direct GLRT approach. Our numerical results show performance improvement in terms of tumor detection probability and Signal to Interference and Noise Ratio (SINR) gain of approximately 2.2 dB at a probability of detection of 0.9 over the GLRT method. |
| first_indexed | 2024-10-01T05:50:32Z |
| format | Conference Paper |
| id | ntu-10356/99490 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T05:50:32Z |
| publishDate | 2013 |
| record_format | dspace |
| spelling | ntu-10356/994902020-03-07T13:24:49Z A Bayesian nonparametric approach to tumor detection using UWB imaging Nijsure, Yogesh Tay, Wee Peng Gunawan, Erry Yue, Joshua Lai Chong School of Electrical and Electronic Engineering IEEE International Conference on Ultra-Wideband (2012 : Syracuse, New York, US) We develop a tumor detection and discrimination algorithm for Ultra-Wideband (UWB) microwave imaging of breast cancer based on a Bayesian nonparametric approach. We model the UWB backscattered signal as a mixture of distinct scatterer contributions, and use a Dirichlet Process mixture model (DPMM) to describe the amplitudes and delays of the backscattered returns. Because of the unbounded complexity afforded by the DPMM, model under-fitting is avoided and parameters like the clutter covariance matrix in other commonly used approaches, need not be estimated. The DPMM allows us to perform discrimination when there are multiple tumor and clutter sources that present as extended radar targets. After performing discrimination, we distinguish the tumor sources from other clutter sources using a generalized likelihood ratio test (GLRT). We perform experiments on a breast phantom with realistic dielectric contrast ratios, and compare the performance of our algorithm with a direct GLRT approach. Our numerical results show performance improvement in terms of tumor detection probability and Signal to Interference and Noise Ratio (SINR) gain of approximately 2.2 dB at a probability of detection of 0.9 over the GLRT method. 2013-08-02T04:40:47Z 2019-12-06T20:08:02Z 2013-08-02T04:40:47Z 2019-12-06T20:08:02Z 2012 2012 Conference Paper https://hdl.handle.net/10356/99490 http://hdl.handle.net/10220/12892 10.1109/ICUWB.2012.6340410 en |
| spellingShingle | Nijsure, Yogesh Tay, Wee Peng Gunawan, Erry Yue, Joshua Lai Chong A Bayesian nonparametric approach to tumor detection using UWB imaging |
| title | A Bayesian nonparametric approach to tumor detection using UWB imaging |
| title_full | A Bayesian nonparametric approach to tumor detection using UWB imaging |
| title_fullStr | A Bayesian nonparametric approach to tumor detection using UWB imaging |
| title_full_unstemmed | A Bayesian nonparametric approach to tumor detection using UWB imaging |
| title_short | A Bayesian nonparametric approach to tumor detection using UWB imaging |
| title_sort | bayesian nonparametric approach to tumor detection using uwb imaging |
| url | https://hdl.handle.net/10356/99490 http://hdl.handle.net/10220/12892 |
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