volume | PIER Journals

Abstract

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.

1. Lim, H. B., N. T. Nhung, E. Li, and N. D. Thang, "Confocal microwave imaging for breast cancer detection: Delay-multiply-and-sum image reconstruction algorithm," IEEE Transactions on Biomedical Engineering, Vol. 55, No. 6, 1697-1704. Google Scholar

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.
doi:10.1109/TBME.2002.800759 Google Scholar

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.
doi:10.2528/PIER10030902 Google Scholar

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. Google Scholar

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.
doi:10.2528/PIER10071002 Google Scholar

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.
doi:10.2528/PIERC09031202 Google Scholar

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.
doi:10.2528/PIER10090205 Google Scholar

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.
doi:10.2528/PIER08062701 Google Scholar

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.
doi:10.2528/PIER05081802 Google Scholar

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.
doi:10.1109/TMTT.2004.832686 Google Scholar

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.
doi:10.1109/TAP.2009.2019856 Google Scholar

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.
doi:10.2528/PIER09061004 Google Scholar

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. Google Scholar

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.
doi:10.2528/PIER09100705 Google Scholar

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.
doi:10.2528/PIER10110102 Google Scholar

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. Google Scholar

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. Google Scholar

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.
doi:10.1109/42.650876 Google Scholar

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.
doi:10.2528/PIER10051904 Google Scholar

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.
doi:10.1109/TBME.2007.900564 Google Scholar

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. Google Scholar

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. Google Scholar

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. Google Scholar

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.
doi:10.1109/8.774131 Google Scholar

25. CST Microwave Studio, CST Inc., 2009.

26. Time Domain Corporation, Cummings Research Park, 330 Wynn Drive, Suite 300, Huntsville, AL 35805, USA . Google Scholar

27. Dielectric Constants of Common Materials http: //www.flowme-terdirectory.com/dielectric constant 01.html. Google Scholar

Dr. Saleh Ali AlShehri

Prof. Sabira Khatun

Dr. Adznan B. Jantan

Dr. Raja Syamsul Azmir Raja Abdullah

Dr. Rozi Mahmud

Dr. Zaiki Awang