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Reseach Article

An Automated Classification of Microcalcification Clusters in Mammograms using Dual Tree M-Band Wavelet Transform and Support Vector Machine

by C. Suba, K. Nirmala
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 115 - Number 20
Year of Publication: 2015
Authors: C. Suba, K. Nirmala
10.5120/20269-2678

C. Suba, K. Nirmala . An Automated Classification of Microcalcification Clusters in Mammograms using Dual Tree M-Band Wavelet Transform and Support Vector Machine. International Journal of Computer Applications. 115, 20 ( April 2015), 24-29. DOI=10.5120/20269-2678

@article{ 10.5120/20269-2678,
author = { C. Suba, K. Nirmala },
title = { An Automated Classification of Microcalcification Clusters in Mammograms using Dual Tree M-Band Wavelet Transform and Support Vector Machine },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 115 },
number = { 20 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 24-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume115/number20/20269-2678/ },
doi = { 10.5120/20269-2678 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:55:25.077270+05:30
%A C. Suba
%A K. Nirmala
%T An Automated Classification of Microcalcification Clusters in Mammograms using Dual Tree M-Band Wavelet Transform and Support Vector Machine
%J International Journal of Computer Applications
%@ 0975-8887
%V 115
%N 20
%P 24-29
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Breast cancer is the second leading cause of cancer deaths after lung cancer. In order to avoid mortality due to breast cancer, an efficient computer aided diagnosis system for early prediction of breast cancer is needed. In this paper, an efficient computerized system is designed for the classification of Microcalcification Clusters (MC) in digitized mammograms. The proposed system uses Dual Tree M-Band Wavelet Transform (DTMBWT) to represent the digital mammogram in a multiresolution manner and Support Vector Machine (SVM) for classification. The extracted sub band energies from DTMBWT decomposed mammograms are used as distinguishable features for the classification of MCs into either malignant or benign by SVM classifier. The results show that the proposed DTMBWT based classification system achieves 91. 83%accuracy on Mammographic Image Analysis Society (MIAS) database images.

References
  1. Priya, D, and Muthulekshmi M, 2015. Review of cancer statistics in India. International Journal of Advances in Signal and Image Sciences, 1(1), 1-4.
  2. Lakshmi, S. RN. V. S, and Manoharan, C, 2011. Wavelet Analysis and Orthogonal Moments based Classification of Microcalcification in Digital Mammograms. Journal of Computer Science, 7(10), 1541-1544.
  3. Manoharan, C. , and Lakshmi, S. RN. V. S. 2010. Classification of Micro Calcifications in Mammogram using Combined Feature Set with SVM. International Journal of Computer Applications. 11(10), 30–34.
  4. Lakshmi, S. RN. V. S, and Manoharan, C. 2011. An automated system for classification of micro calcification in mammogram based on Jacobi moments. International Journal of Computer Theory and Engineering, 3(3), 431-434.
  5. Balakumaran, T. , and Vennila, ILA, and Gowri Shankar, C. 2010. Detection of Microcalcification in Mammograms Using Wavelet Transform and Fuzzy Shell Clustering. International Journal of Computer Science and Information Security, 7(1), 121-125.
  6. Bose, J. S. C. K. R, Kumar, S and Karnan, M. 2012. Detection of Microcalcification in Mammograms using Computing Techniques. European Journal of Scientific Research, 86(1), 103-122.
  7. Bhanumathi, R. , and Suresh, G. R. 2013. Detection of Microcalcification in Mammogram Images using Support Vector Machine based Classifier. ITSI Transactions on Electrical and Electronics Engineering, 1(2), 2320-8945.
  8. El-Naqa, I. , Yang, Y. , Wernick, M. N. , Galatsanos, N. P. , and Nishikawa, R. M. 2013. Improvement of Automated Detection Method for Clustered Microcalcification Based on Wavelet Transformation and Support Vector Machine. International Journal of Advanced Research in Artificial Intelligence, 2(4), 23-28.
  9. Jasmine, J. S. L. , Govardhan, A. , Baskaran. S. 2010. Classification of Microcalcification in Mammograms using Nonsubsampled Contourlet Transform and Neural Network. European Journal of Scientific Research, 46 (4). 531-539.
  10. Dehghani, S. , and Dezfooli, M. A. 2011. Breast Cancer Diagnosis System Based on Contourlet Analysis and Support Vector Machine. World Applied Sciences Journal, 13(5), 1067-1076.
  11. Faye, I. 2010. Breast cancer diagnosis in digital mammogram using multiscalecurvelet transforms. Conference on Computerized Medical Imaging and Graphics, 269-276.
  12. Kingsbury, N. 2001. Complex wavelets for shift invariant analysis and filtering of signals. Applied and computational harmonic analysis, 10(3), 234. 253.
  13. Selesnick, I. W. 2004. The double-density dual-tree DWT. IEEE Transaction on Signal Processing, 52(5), 1304-1314.
  14. Chaux, C. , Duval, L. , and Pesquet, J. C. 2006. Image Analysis using a Dual-Tree M-Band Wavelet Transform. IEEE Transactions on Image Processing, 15(8), 2397-2412.
  15. El-Naqa, I. , Yang, Y. , Wernick, M. N. , Galatsanos, N. P. , and Nishikawa, R. 2002. Support vector machine learning for detection of microcalcifications in mammograms. IEEE International Symposium on Biomedical Imaging, 201-204.
Index Terms

Computer Science
Information Sciences

Keywords

Digital mammography microcalcification benign malignant wavelet transform support vector machine.