MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE

Yıl 2009, Cilt: 9 Sayı: 1, 867 - 875, 14.02.2012

Pelin Gorgel Ahmet Sertbaş Niyazi Kılıc Osman N. Ucan Onur Osman

https://izlik.org/JA87BX37ND

Öz

MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE

Anahtar Kelimeler

Discrete Wavelet Transform , Mammographic Mass Classification , Support Vector

Kaynakça

• [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.