Buradasınız

Anasayfa » Content

MAMMOGRAPHIC MASS CLASSIFICATION USING WAVELET BASED SUPPORT VECTOR MACHINE

Journal Name:

  • Istanbul University Journal of Electrical & Electronics Engineering

Publication Year:

  • 2009

Key Words:

Author NameUniversity of AuthorFaculty of Author
Abstract (2. Language): 
In this paper, we investigate an approach for classification of mammographic masses as benign or malign. This study relies on a combination of Support Vector Machine (SVM) and wavelet-based subband image decomposition. Decision making was performed in two stages as feature extraction by computing the wavelet coefficients and classification using the classifier trained on the extracted features. SVM, a learning machine based on statistical learning theory, was trained through supervised learning to classify masses. The research involved 66 digitized mammographic images. The masses were segmented manually by radiologists, prior to introduction to the classification system. Preliminary test on mammogram showed over 84.8% classification accuracy by using the SVM with Radial Basis Function (RBF) kernel. Also confusion matrix, accuracy, sensitivity and specificity analysis with different kernel types were used to show the classification performance of SVM.
Bookmark/Search this post with
FULL TEXT (PDF): 
PDF icon arastirmax-mammographic-mass-classification-using-wavelet-based-support-vector-machine.pdf
  • 1
867-875
  • English

REFERENCES

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. Gunes, “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.
Mammographic Mass Classification Using Wavelet Based Support Vector Machine
Pelin GORGEL , Ahmet SERTBAS, Niyazi KILIC, Osman N. UCAN, Onur OSMAN
875
[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.

Thank you for copying data from http://www.arastirmax.com