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MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE

Year 2009, Volume: 9 Issue: 1, 867 - 875, 14.02.2012

Abstract

MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE

References

  • [1] B. Sahiner, H.P. Chan, “Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images”, IEEE T. Med Imaging, 15:598- 610, 1996.
  • [2] C.C. Boring, T.S. Squires, “Cancer statistics”, CA-A Cancer J Clin, 44:7-26, 1994.
  • [3] H.C. Zuckerman, “The role of mammograph in the diagnosis of breast cancer, in Breast Cancer, Diagnosis and Treatment”, I. M. Ariel and J. B. Cleary. Eds. McGraw-Hill, New York, 1987.
  • [4] H.P. Chan, K. Doi, “Improvement in radiologists’ detection of clustered microcalcifications on mammograms: the potential of computer-aided diagnosis”, Invest Radiol, 25:1102-1110,1990.
  • [5] Wang T C, Karayiannis N B, “Detection of microcalcifications in digital mammograms using wavelets” IEEE T Med Imaging, 17:498- 509, 1989.
  • [6] Bruce L M, Adhami R R, “Classifying mammographic mass shapes using the wavelet transform modulus-maxima method”, IEEE T Med Imaging 18:1170-1177, 1999.
  • [7] S.G. Mallat, “A theory of multiresolution signal decomposition: the wavelet representation”, IEEE T. Pattern Anal, 1980, 11:674– 693.
  • [8] R.N. Bracewell, “The Fourier Transform and its Applications”, McGraw-Hill, New York, 1999.
  • [9] V. Vapnik, “Statistical learning theory”, Wiley, New York, 1998.
  • [10] K.R. Muller, S. Mika, “An introduction to kernel-based learning algorithms”, IEEE T., Neural Network, 2001, 12:181-201.
  • [11] I. El-Naqa, Y. Yang, “A support vector machine approach for detection of microcalcifications in mammogram”, IEEE T. Med Imaging, 2002, 21:1552-1563.
  • [12] C.M. Brislawn, “Fingerprints go digital”, Notices Amer Math Soc., 1995, 42:1278-1283.
  • [13] M. Al-qdah, A.R. Ramli, “Detection of calcifications in mammography using wavelets”, Student Conference on Research and Development (SCORcD) Proceedings, 2003, Putrajaya, Malaysia.
  • [14] Mathsoft Wavefet resources, A great collection of theory and application oriented articles on the web at http://mw.mthsof.cod wavelets.html, 1997.
  • [15] S. Chaplot, L.M. Patnaik, “Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network”, Biomedical Signal Processing and Control, 2006, 1:86-92.
  • [16] R.C. Gonzalez, R.E. Woods, “Digital Image Processing”, Prentice Hall, New Jersey, 2002.
  • [17] Koenderink J (1984) The structure of images. Biol Cybern 50:363-370.
  • [18] V.N. Vapnik, “An overview of statistical learning theory”, IEEE T. Neural Network, 1999 10:988-999.
  • [19] K. Polat, S. Guneş, “Breast cancer diagnosis using least square support vector machine”, Digital Signal Process. “in press”, 2006.
  • [20] E.D. Übeyli, “ECG beats classification using multiclass support vector machines with error correcting output codes”, Digital Signal Process.,17:675-684, 2007.
  • [21] A. Bazzani, A.D. Bevilacqua, “Automatic detection of clustered microcalcifications in digital mammograms using an SVM classifier”, ESANN’2000 proceedings, European Symposium on Artificial Neural Networks, Bruges, Belgium, 2000.
  • [22] P.A. Devijver, J. Kittler, “Pattern Recognition: A Statistical Approach”, Prentice-Hall, London, 1982.
  • [23] C.C. Chang, C.J. Lin, LIBSVM:a library for support vector machines, Software available athttp://www.csie.ntu.edu.tw/~cjilin/libsvm2001.
  • [24] A.F. Laine, S., “Schuler Mammographic feature enhancement by multiscale analysis”, IEEE T. Med Imaging, 13:725-740,1994.
Year 2009, Volume: 9 Issue: 1, 867 - 875, 14.02.2012

Abstract

References

  • [1] B. Sahiner, H.P. Chan, “Classification of mass and normal breast tissue: a convolution neural network classifier with spatial domain and texture images”, IEEE T. Med Imaging, 15:598- 610, 1996.
  • [2] C.C. Boring, T.S. Squires, “Cancer statistics”, CA-A Cancer J Clin, 44:7-26, 1994.
  • [3] H.C. Zuckerman, “The role of mammograph in the diagnosis of breast cancer, in Breast Cancer, Diagnosis and Treatment”, I. M. Ariel and J. B. Cleary. Eds. McGraw-Hill, New York, 1987.
  • [4] H.P. Chan, K. Doi, “Improvement in radiologists’ detection of clustered microcalcifications on mammograms: the potential of computer-aided diagnosis”, Invest Radiol, 25:1102-1110,1990.
  • [5] Wang T C, Karayiannis N B, “Detection of microcalcifications in digital mammograms using wavelets” IEEE T Med Imaging, 17:498- 509, 1989.
  • [6] Bruce L M, Adhami R R, “Classifying mammographic mass shapes using the wavelet transform modulus-maxima method”, IEEE T Med Imaging 18:1170-1177, 1999.
  • [7] S.G. Mallat, “A theory of multiresolution signal decomposition: the wavelet representation”, IEEE T. Pattern Anal, 1980, 11:674– 693.
  • [8] R.N. Bracewell, “The Fourier Transform and its Applications”, McGraw-Hill, New York, 1999.
  • [9] V. Vapnik, “Statistical learning theory”, Wiley, New York, 1998.
  • [10] K.R. Muller, S. Mika, “An introduction to kernel-based learning algorithms”, IEEE T., Neural Network, 2001, 12:181-201.
  • [11] I. El-Naqa, Y. Yang, “A support vector machine approach for detection of microcalcifications in mammogram”, IEEE T. Med Imaging, 2002, 21:1552-1563.
  • [12] C.M. Brislawn, “Fingerprints go digital”, Notices Amer Math Soc., 1995, 42:1278-1283.
  • [13] M. Al-qdah, A.R. Ramli, “Detection of calcifications in mammography using wavelets”, Student Conference on Research and Development (SCORcD) Proceedings, 2003, Putrajaya, Malaysia.
  • [14] Mathsoft Wavefet resources, A great collection of theory and application oriented articles on the web at http://mw.mthsof.cod wavelets.html, 1997.
  • [15] S. Chaplot, L.M. Patnaik, “Classification of magnetic resonance brain images using wavelets as input to support vector machine and neural network”, Biomedical Signal Processing and Control, 2006, 1:86-92.
  • [16] R.C. Gonzalez, R.E. Woods, “Digital Image Processing”, Prentice Hall, New Jersey, 2002.
  • [17] Koenderink J (1984) The structure of images. Biol Cybern 50:363-370.
  • [18] V.N. Vapnik, “An overview of statistical learning theory”, IEEE T. Neural Network, 1999 10:988-999.
  • [19] K. Polat, S. Guneş, “Breast cancer diagnosis using least square support vector machine”, Digital Signal Process. “in press”, 2006.
  • [20] E.D. Übeyli, “ECG beats classification using multiclass support vector machines with error correcting output codes”, Digital Signal Process.,17:675-684, 2007.
  • [21] A. Bazzani, A.D. Bevilacqua, “Automatic detection of clustered microcalcifications in digital mammograms using an SVM classifier”, ESANN’2000 proceedings, European Symposium on Artificial Neural Networks, Bruges, Belgium, 2000.
  • [22] P.A. Devijver, J. Kittler, “Pattern Recognition: A Statistical Approach”, Prentice-Hall, London, 1982.
  • [23] C.C. Chang, C.J. Lin, LIBSVM:a library for support vector machines, Software available athttp://www.csie.ntu.edu.tw/~cjilin/libsvm2001.
  • [24] A.F. Laine, S., “Schuler Mammographic feature enhancement by multiscale analysis”, IEEE T. Med Imaging, 13:725-740,1994.
There are 24 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Pelin Gorgel This is me

Ahmet Sertbaş This is me

Niyazi Kılıc This is me

Osman N. Ucan This is me

Onur Osman This is me

Publication Date February 14, 2012
Published in Issue Year 2009 Volume: 9 Issue: 1

Cite

APA Gorgel, P., Sertbaş, A., Kılıc, N., Ucan, O. N., et al. (2012). MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE. IU-Journal of Electrical & Electronics Engineering, 9(1), 867-875.
AMA Gorgel P, Sertbaş A, Kılıc N, Ucan ON, Osman O. MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE. IU-Journal of Electrical & Electronics Engineering. February 2012;9(1):867-875.
Chicago Gorgel, Pelin, Ahmet Sertbaş, Niyazi Kılıc, Osman N. Ucan, and Onur Osman. “MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE”. IU-Journal of Electrical & Electronics Engineering 9, no. 1 (February 2012): 867-75.
EndNote Gorgel P, Sertbaş A, Kılıc N, Ucan ON, Osman O (February 1, 2012) MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE. IU-Journal of Electrical & Electronics Engineering 9 1 867–875.
IEEE P. Gorgel, A. Sertbaş, N. Kılıc, O. N. Ucan, and O. Osman, “MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE”, IU-Journal of Electrical & Electronics Engineering, vol. 9, no. 1, pp. 867–875, 2012.
ISNAD Gorgel, Pelin et al. “MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE”. IU-Journal of Electrical & Electronics Engineering 9/1 (February 2012), 867-875.
JAMA Gorgel P, Sertbaş A, Kılıc N, Ucan ON, Osman O. MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE. IU-Journal of Electrical & Electronics Engineering. 2012;9:867–875.
MLA Gorgel, Pelin et al. “MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE”. IU-Journal of Electrical & Electronics Engineering, vol. 9, no. 1, 2012, pp. 867-75.
Vancouver Gorgel P, Sertbaş A, Kılıc N, Ucan ON, Osman O. MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE. IU-Journal of Electrical & Electronics Engineering. 2012;9(1):867-75.