Breast Cancer Detection Using Support Vector Machine Technique Applied on Extracted Electromagnetic Waves
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Breast Cancer Detection Using Support Vector Machine Technique Applied on Extracted Electromagnetic WavesAbstract
Breast cancer is one of the most common kinds of cancer, as well as the leading cause of decease among women. Early detection and diagnosis of breast cancer increases the chances for successful treatment and complete recovery for the patient. Mammography is currently the most sensitive method to detect early breast cancer; however, the magnetic resonance imaging (MRI) is the most attractive alternative to mammogram. Manual readings of mammograms may result in misdiagnosis due to human errors caused by visual fatigue. Computer aided detection systems (CAD) serve as a second opinion for radiologists. A new CAD system for the detection of breast cancer in mammograms is proposed. The discrete wavelet transform (DWT), the contourlet transform, and the principal component analysis (PCA) are all used for feature extraction; while the support vector machine (SVM) is used for classification.The system classifies normal and abnormal tissues in addition to benign and malignant tumors. A further investigation was implemented using electromagnetic waves instead of the classical MRI approach. A breast model was generated and near field data of electromagnetic waves were extracted to detect the abnormalities in the breast, especially the masses.
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S. Omar, H. Khaled, R. Gaafar, A. R. Zekry, S.
Eissa, and O. El -Khatib , “Breast Cancer in
Egypt: A Review of Disease Presentation and
Detection Strategies,” La Revue de Santé de la
Méditerranéeorientale, vol. 9, no. 3, 2003.
J. Bozek, M. Mustra, K. Delac, and M. Grgic,
“A Survey of Image Processing Algorithms in
Digital Mammography,” Rec. Advan. In Mult.
Sig. Process. And Commun., SCI 231, pp. 631-
, 2009.
T. Bhangale, U. B. Desai, and U. Shama, “An
Unsupervised Scheme for Detection of
Microcalcifications on Mammograms,” IEEE
Image Processing proceedings International
Conference, vol. 1, 2000.
R. N. Strickland and H. I. Hahn, “Wavelet
Transforms for Detecting Microcalcifications in
Mammogram,” IEEE Trans. Med. Imaging, vol.
, no. 2, pp. 218-229, April 1996.
T. C. Wang and N. B. Karayiannis, “Detection
of Microcalcifications in Digital Mammograms
using Wavelets,” IEEE Trans. Med. Imaging,
vol. 17 no. 4, pp. 498–509, August 1998.
L. M. Bruce and R. R. Adhami, “Classifying
Mammographic Mass Shapes using the Wavelet
Transform Modulus-Maxima Method,” IEEE
Trans. Med. Imaging, vol. 18, pp. 1170-1177,
H. Yoshida, K. Doi, and M. Nishikawa,
“Automated Detection of Clustered
Microcalcifications in Digital Mammograms
using Wavelet Transform Techniques,” Medical
Imaging Bellingham Proc. SPIE 2167, 1994.
D. Gunawan, “Microcalcification Detection
using Wavelet Transform,” IEEE
Communications, Computers and signal
Processing Conference, 2001.
L. C. Juarez, J. V. Ponomaryov, and R. L.
Sanchez, “Detection of Microcalcifications in
Digital Mammograms Images using Wavelet
Transform,” IEEE Proceedings of the
Electronics, Robotics and Automotive Mechanics
Conference, 2006.
T. Balakumaran, I. Vennila, and C. G. Shankar,
“Detection of Microcalcification in
Mammograms using Wavelet Transform and
Fuzzy Shell Clustering,” International Journal of Computer Science and Information Security
(IJCSIS), vol. 7, no. 1, 2010.
J. A. M. Lizcano, C. S. Avila, and L. M. Perez,
“A Microcalcification Detection System for
Digital Mammography using the Contourlet
Transform,” Proceeding of the International
conference on Computational and Experimental
Engineering and Science, Portugal, July 2004.
J. F. D. Addison, S. Wermter, and G. Z. Arevian,
“A Comparison of Feature Extraction and
Selection Techniques,” Proc. Int’l Conf. on
Artificial Neural Networks 2003, Istanbul,
Turkey, pp. 212-215, June 2003.
Y. I. A. Rejani and S. T. Selvi, “Early Detection
of Breast Cancer using SVM Classifier
Technique,” International Journal on Computer
Science and Engineering, vol. 1, no. 3, 2009.
M. Sharkas, M. Al Sharkawy, and D. Ragab,
“Detection of Microcalcifications in
Mammograms using Support Vector Machine,”
IEEE European Modeling Symposium
EMS2011, Madrid, Spain, November 2011.
S. M. Pizer, et al. “Adaptive Histogram
Equalization and its Variations,” Computer
Vision, Graphics, and Image Processing, vol.
, 1987.
E. D. Pisano et al., “Contrast Limited Adaptive
Histogram Equalization Image Processing to
Improve the Detection of Simulated Spiculations
in Dense Mammograms,” Digital Imaging, vol.
, no. 4, pp. 193-200, 1998.
Digital database for screening mammography
(DDSM). Available online at:
http://marathon.csee.usf.edu/mammograhy/
Database.html.
H. Mirzaalian, M. R. Ahmadzadeh, S. Sadri, and
M. Jafari, “Various Applying of Wavelet
Transform in Digital Mammograms for
Detecting Masses and Microcalcifications,”
Conference on Machine Vision Applications,
Tokyo, Japan, May 2007.
T. Edwards, “Discrete Wavelet Transforms
Theory and Implementation,” Stanford
University, September 1992. Available online at:
http://qss.stanford.edu/~godfrey/wavelets
M. N. Do and M. Vetterli, “The Contourlet
Transform: An Efficient Directional
Multiresolution Iimage Representation,” IEEE
Trans. Image processing, vol. 14, no. 12, pp.
-2106, December 2005.
P. J. Burt and E. H. Adelson, “The Laplacian
Pyramid as a Compact Image Code,” IEEE
Trans. Communications, v ol. COM-31, no. 4,
April 1983.
A. P. N. Vo, T. T. Nguyen, and S. Oraintara,
“Texture Image Retrieval using Complex
Directional Filterbank,” IEEE Proceeding of the
International Symposium Circuits and Systems,
ISCAS 2006.
M. N. Do and M. Vetterli, “Contourlets, in
Beyond Wavelets,” G. V. Welland, Ed.
Amsterdam, Netherlands: Academic, chapter 4,
pp. 83–105, 2003.
L. I. Smith, “A Tutorial on Principal
Components Analysis,” February 2002.
Available online at:
www.cs.otago.ac.nz/cosc453/student
.../principal_components.pdf
S. R. Gunn, “Support Vector Machines for
Classification and Regression,” Technical
Report, Faculty of Engineering, Science and
Mathematics, School of Electronics and
Computer Science, May 1998.
I. El-Naqa, Y. Yang, M. N. Wernick, N. P.
Galatsanos, and R. M. Nishikawa, “A Support
Vector Machine Approach for Detection of
Microcalcifications,” IEEE Trans. Med.
Imaging, vol. 21, no. 12, December 2002.
G. Zhu, M. Popovic, “Enhancing Microwave
Breast Tomography with Microwave Induced
Thermoacoustic Imaging,” Applied
Computational Electromagnetics Society
(ACES) Journal, vol. 24, no. 6, pp. 618-627,
December 2009.
S. Iudicello, F. Bardati, “Functional Imaging of
Compressed Breast by Microwave Radiometry,”
Applied Computational Electromagnetics
Society (ACES) Journal, vol. 24, no. 1, pp. 64 –
, February 2009.
D. A. Woten and M. El-Shenawee, “Quantitative
Analysis of Breast Skin for Tumor Detection
using Electromagnetic Waves,” Applied
Computational Electromagnetics Society
(ACES) Journal, vol. 24, no. 5, pp. 458-463,
October 2009.
H. Kanj and M. Popovic, “Two-Element T-
Array for Cross-Polarized Breast
Tumor Detection,” Applied Computational
Electromagnetics Society (ACES) Journal, vol.
, no. 3, pp. 249-254, September 2008.
R. Weisskoff, “MRImages,” Available online at:
int.ch.liv.ac.uk
HFSSTM Software, Version 9, Ansoft
Corporation.
