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Article:An Automated Mass Classification System in Digital Mammograms using Contourlet Transform and Support Vector Machine

by J.S.Leena Jasmine, Dr.S.Baskaran, Dr.A.Govardhan
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 31 - Number 9
Year of Publication: 2011
Authors: J.S.Leena Jasmine, Dr.S.Baskaran, Dr.A.Govardhan
10.5120/3857-5375

J.S.Leena Jasmine, Dr.S.Baskaran, Dr.A.Govardhan . Article:An Automated Mass Classification System in Digital Mammograms using Contourlet Transform and Support Vector Machine. International Journal of Computer Applications. 31, 9 ( October 2011), 54-61. DOI=10.5120/3857-5375

@article{ 10.5120/3857-5375,
author = { J.S.Leena Jasmine, Dr.S.Baskaran, Dr.A.Govardhan },
title = { Article:An Automated Mass Classification System in Digital Mammograms using Contourlet Transform and Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { October 2011 },
volume = { 31 },
number = { 9 },
month = { October },
year = { 2011 },
issn = { 0975-8887 },
pages = { 54-61 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume31/number9/3857-5375/ },
doi = { 10.5120/3857-5375 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:17:45.576374+05:30
%A J.S.Leena Jasmine
%A Dr.S.Baskaran
%A Dr.A.Govardhan
%T Article:An Automated Mass Classification System in Digital Mammograms using Contourlet Transform and Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 31
%N 9
%P 54-61
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, an efficient automated mass classification system for breast cancer in digitized mammograms using NonSubsampled Contourlet Transform (NSCT) and Support Vector Machine (SVM) is presented. The classification of masses is achieved by extracting the mass features from the contourlet coefficients of the image and the outcomes are used as an input to the SVM classifier for classification. The system classifies the mammogram images as normal or abnormal, and the abnormal severity as benign or malignant. The evaluation of the system is carried on using mammography image analysis society (MIAS) database. The experimental result shows that the proposed method provides improved classification rate.

References
  1. N.R. Pal , B. Bhowmick, S.K. Patel ,S. Pal “ A multistage neural network aided system for detection of microcalcification in digital mammograms,” Nerocomputing 71(2008) ,2625-2634.
  2. J.C. Fu, S.K. Lee , S.T.C Wong , J.Y. Yeh, A.H. Wang, H.K. Wu, “ Image segmentation feature selection and pattern classifications for mammographic microcalcifications”, Computerized medical imaging and graphics 29 (2005),419-429.
  3. Polakowski WE,Cournoyer DA ,Rogers SK , Desimio MP, Ruck DW , Hoffmeister JW, Ralines RA “ Computer – Aided breast cancer detection and diagnosis of masses using difference of Gaussian and derivative based feature saliency” , IEEE transactions on medical imaging , Vol 16(6) , December 1997.
  4. J.A.M.Van Dijck, L.M.Verbeek, J. H.C.L. H Hendrinks, “The current detectability of breast cancer in a mammographic screening programs cancer”, PubMed-indexed 72(6) (1993).1933-1938
  5. H.D. Cheng, X.J. Shi, R. Min, L.M. Hu, X.P. Cai, H.N. Du (2006) “Approaches for automated detection and classification of masses in mammograms”, Pattern Recognition, Vol. 39, pp. 646-668.
  6. R. O. Duda, P. E. Hart, D. G. Stork (2001), Pattern Classification, John Wiley and Sons, second edition.
  7. H.P. Chan, D. Wei, M.A. Helvie, B. Sahiner et.al. (1995) “Computer-aided classification of mammographic masses and normal tissue: linear discriminant analysis in texture feature space”, Journal of Physics in Medicine and Biology, Vol. 40, pp. 857-876.
  8. B. Sahiner, N. Petrick, H.P. Chan (2001) “Computer-aided characterization of mammographic masses: accuracy of mass segmentation and its effects on characterization”, IEEE Trans. Med. Imaging, Vol. 20, No. 12, pp. 1275–1284.
  9. P.J.G. Lisboa, (2000) “A review of evidence of health benefits from artificial neural networks in medical intervention”, Neural Networks, Vol. 15, pp. 11-39.
  10. J.L. Viton, M. Rasigni, G. Rasigni, A. Liebaria (1996) “Method for characterizing masses in digital mammograms”, Opt. Eng., Vol. 35, No. 12, pp. 3453–3459.
  11. Y. Alginahi (2004) “Computer analysis of composite documents with non-uniform background”,PhD Thesis, Electrical and Computer Engineering, University of Windsor, Windsor, ON, Canada.
  12. B. Zheng, Y.H. Chang, X.H. Wang, W.F. Good (1999) “Comparison of artificial neural network and Bayesian belief network in a computer assisted diagnosis scheme for mammography”, IEEE International Conference on Neural Networks, pp. 4181–4185.
  13. B. Sahiner, H.P. Chan, N. Petrick, M.A. Helvie, M.M. Goodsitt (1998) “Design of a high-sensitivity classifier based on a genetic algorithm: application to computer-aided diagnosis”, Phys. Med. Biol.Vol. 43, No. 10, pp. 2853–2871.
  14. H.P. Chan, K.Doi, S.Galhotra, C.J.llVyborny, H.Macmahon & P.M.Jokich, “Image Feature Analysis and Computer-Aided Diagnosis in Digital Radiography”, Medi.Phy., Jul-Aug 1987,Volume 14,pp 538-548.
  15. Davies DH, Dance DR. “Automated computer detection of clustered calcifications in digital mammogram “, Physics in Medicine and Biology. Volume.35,Aug -1990, pp.1111–1118.
  16. Cheng H, Lui Ym ,Felimans RI, “ A noval approach to microcalcification detection using fuzzy logic Technique”, IEEE Transction Medical Image, Volume 17, Jun -1998, pp 442-450.
  17. Kassemeijier N, “Adaptive noise equalization and recognition of microcalcifications in mammography”. Inter .J. Pattern Recog. Arti. Intel , Volume 687, 1993, pp1357-76. from 1997 to 1999) and Ve
  18. Bazzani A, Bevilacqua a, Bollini D, Brancaaio R, Camanini R , Lancoonelli N et al “Automated detection of clustered microcalcifications in digital mammograms using an SVM classifier”, In Proceedings of the European Symposium on Artificial Neural Networks, April 2000, pp 195-200.
  19. Zaiane, O, Maria-Luiza, A & Alexandru “Application of data mining technique for medical image classification”, In proceedings of second International Workshop on Multimedia, USA.2001. pp 94-101.
  20. Chan HP, Lo SCB Sahiner B, Lam Helvie MA. “Computer aided detection of mammographic microcalcifications: pattern recognition with an artificial neural network”, Medical . Physics, Volume 22, Oct-1995, pg 1555- 67.
  21. Nigel RH , Nishikawa RM, Papaioannou J, Doi K, “ Analysis of methods for reducing false positives in the automated detection of clustered microcalcifications in mammograms”. Medical. Physics, Volume 25, Aug - 1998, pg 1502-1506.
  22. M.N.Do & M.Vetterli, “The contourlet transform: An efficient directional multiresolution image representation,” IEEE Trans. Image Proc., IEEE Transactions on ImageProcessing, Volume 14, Dec. 2004, pp2091-2106.
  23. P. J. Burt and E. H. Adelson, “The Laplacian pyramid as a compact image code,” IEEE Trans. Commun., vol. 31, no. 4, pp. 532–540, April 1983.
  24. M. N. Do and M. Vetterli, “Framing pyramids,” IEEE Trans. Signal Process, Volume. 51,Sep 2003, pp. 2329–2342.
  25. R. H. Bamberger and M. J. T. Smith, “A filter bank for the directional decomposition of images: Theory and design,” IEEE Trans. Signal Proc., Volume 40,april 1992, pp. 882–893
  26. Y.Ireaneus Anna Rejani, S.Thamarai Selvi “Early detection of breast cancer using SVM classifier technique” International Journal on Computer Science and Engineering Vol.1(3), 2009, 127-130
  27. Smola A. J., Scholkopf B., and Muller K. R., “The connection between regularization operators and support vector kernels”, Neural Networks New York, vol.11, November 1998, pg 637-649.
  28. MIAS database, http://peipa.essex.ac.uk/ipa/pix/mias/
Index Terms

Computer Science
Information Sciences

Keywords

Mammogram Mass classification Benign Malignant NSCT SVM

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