This paper presents both simulation and experimental study to detect and locate breast tumors along with their classification as malignant and/or benign in three dimensional (3D) breast model. The contrast between the dielectric properties of these two tumor types is the main key. These dielectric properties are mainly controlled by the water and blood content of tumors. For simulation, electromagnetic simulator software is used. The experiment is conducted using commercial Ultrawide-Band (UWB) transceivers, Neural Network (NN) based Pattern Recognition (PR) software for imaging and homogenous breast phantom. The 3D homogeneous breast phantom and tumors are fabricated using pure petroleum jelly and a mixture of wheat flour and water respectively. The simulation and experimental setups are performed by transmitting the UWB signals from one side of the breast model and receiving from opposite side diagonally. Using discrete cosine transform (DCT) of received signals, we have trained and tested the developed experimental Neural Network model. In 3D breast model, the achieved detection accuracy of tumor existence is around 100%, while the locating accuracy in terms of (x,y,z) position of a tumor within the breast reached approximately 89.2% and 86.6% in simulation and experimental works respectively. For classification, the permittivity and conductivity detection accuracy are 98.0% and 99.1% in simulation, and 98.6% and 99.5% in experimental works respectively. Tumor detection and type specification 3D may lead to successful clinical implementation followed by saving of precious human lives in the near future.
2. Fear, E. C., X. Li, S. C. Hagness, and M. A. Stuchly, "Confocal microwave imaging for breast tumor detection: Localization of tumors in three dimensions," IEEE Transactions on Biomedical Engineering, Vol. 49, No. 8, 812-822, 2002.
3. O'Halloran, M., M. Glavin, and E. Jones, "Channel-ranked beamformer for the early detection of breast cancer," Progress In Electromagnetic Research, Vol. 103, 153-168, 2010.
4. Byrne, D., M. O'Halloran, M. Glavin, and E. Jones, "Channel-ranked beamformer for the early detection of breast cancer," Progress In Electromagnetic Research, Vol. 103, 153-168, 2010.
5. O'Halloran, M., M. Glavin, and E. Jones, "Rotating antenna microwave imaging system for breast cancer detection," Progress In Electromagnetic Research, Vol. 107, 203-217, 2010.
6. Alshehri, S. A. and S. Khatun, "UWB imaging for breast cancer detection using neural networks," Progress In Electromagnetic Research C, Vol. 7, 79-93, 2009.
7. Byrne, D., M. O'Halloran, E. Jones, and M. Glavin, "Transmitter-grouping robust capon beamforming for breast cancer detection," Progress In Electromagnetic Research, Vol. 108, 401-416, 2010.
8. Zhang, H., S. Y. Tan, and H. S. Tan, "A novel method for microwave breast cancer detection," Progress In Electromagnetics Research, Vol. 83, 413-434, 2008.
9. Bindu, G., A. Lonappan, V. Thomas, C. K. Ananadan, and K. T. Mathew, "Active microwave imaging for breast cancer detection," Progress In Electromagnetic Research, Vol. 58, 149-169, 2006.
10. Li, X., S. K. Davis, S. C. Hagness, D. W. Weide, and B. D. Veen, "Microwave imaging via space-time beam forming: Experimental investigation of tumor detection in multilayer breast phantoms," IEEE Trans. Microwave Theory Techniques, Vol. 52, No. 8, 1856-1865, 2004.
11. Klemm, M., I. Craddock, J. Leendertz, A. Preece, and R. Benjamin, "Radar-based breast cancer detection using a hemispherical antenna array --- Experimental results," IEEE Transactions on Antennas and Propagation, Vol. 57, 1692-1704, 2009.
12. Lazaro, A., D. Girbau, and R. Villarino, "Simulated and experimental investigation of microwave imaging using UWB," Progress In Electromagnetics Research, Vol. 94, 263-280, 2009.
13. Lai, J. C., C. B. Soh, E. Gunawan, and K. S. Low, "Homogeneous and heterogeneous breast phantom for ultra-wideband microwave imaging applications," Progress In Electromagnetic Research, Vol. 100, 377-415, 2010.
14. Lazaro, A., D. Girbau, and R. Villarino, "Wavelet-based breast tumor localization technique using a UWB radar," Progress In Electromagnetic Research, Vol. 98, 75-95, 2009.
15. Alshehri, S. A., S. Khatun, A. Jantan, R. S. A. Raja Abdullah, R. Mahmod, and Z. Awang, "Experimental breast tumor detection using NN-based UWB imaging," Progress In Electromagnetic Research, Vol. 111, 447-465, 2011.
16. Sha, L., E. R. Ward, and B. Story, "A review of dielectric properties of normal and malignant breast tissue," Proceedings IEEE SoutheastCon, 457-462, Apr. 5-7, 2002.
17. Lazebnik, M., et al., "A large-scale study of the ultrawideband microwave dielectric properties of normal, benign and malignant breast tissues obtained from cancer surgeries," Phys. Med. Biol., Vol. 52, 6093-6115, IOP Publishing, Oct. 2007.
18. Rangayyan, R. M., N. M. El-Faramawy, J. E. Leo Desautels, and O. A. Alim, "Measures of acutance and shape for classification of breast tumor ," IEEE Transactions on Medical Imaging,, Vol. 16, No. 6, Dec. 1997.
19. Conceicao, R. C., M. O'Halloran, E. Jones, and M. Glavin, "Investigation of classifiers for early-stage breast cancer based on radar target signatures ," Progress In Electromagnetic Research, Vol. 105, 295-311, 2010.
20. Davis, S. K., B. D. van Veen, S. C. Hagness, and F. Kelcz, "Breast tumor characterization based on ultrawideband microwave backscatter," IEEE Transactions on Biomedical Engineering, Vol. 55, No. 1, Jan. 2008.
21. Insana, M. F., C. Pellot-Barakat, M. Sridhar, and K. K. Lindfors, "Viscoelastic imaging of breast tumor microenvironment with ultrasound," Journal of Mammary Gland Biology and Neoplasia, Vol. 9, No. 4, Oct. 2004.
22. Bindu, G. and K. T. Mathew, "Characterization of benign and malignant breast tissues using 2-D microwave tomographic imaging," Microwave and Optical Technology Letters, Vol. 49, No. 10, Oct. 2007.
23. O'Halloran, M., B. McGinley, R. C. Conceicao, F. Morgan, E. Jones, and M. Glavin, "Spiking neural networks for breast cancer classi¯cation in a dielectrically heterogeneous breast," Progress In Electromagnetics Research, Vol. 113, 413-428, 2011.
24. Hagness, S. C., A. Taflove, and J. E. Bridges, "Three dimensional FDTD analysis of a pulsed microwave confocal system for breast cancer detection design of an antenna-array element," IEEE Transactions on Antennas and Propagation, Vol. 47, No. 5, May 1999.
25. CST Microwave Studio, CST Inc., 2009.
26., Time Domain Corporation, Cummings Research Park, 330 Wynn Drive, Suite 300, Huntsville, AL 35805, USA .
27., Dielectric Constants of Common Materials http: //www.flowme-terdirectory.com/dielectric constant 01.html.