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Reseach Article

Pre-Processing Algorithms on Digital Mammograms

by Sara I. Basheer, Younis M. Abbosh, Majid Dh. Younis
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 174 - Number 6
Year of Publication: 2017
Authors: Sara I. Basheer, Younis M. Abbosh, Majid Dh. Younis

Sara I. Basheer, Younis M. Abbosh, Majid Dh. Younis . Pre-Processing Algorithms on Digital Mammograms. International Journal of Computer Applications. 174, 6 ( Sep 2017), 16-19. DOI=10.5120/ijca2017915415

@article{ 10.5120/ijca2017915415,
author = { Sara I. Basheer, Younis M. Abbosh, Majid Dh. Younis },
title = { Pre-Processing Algorithms on Digital Mammograms },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2017 },
volume = { 174 },
number = { 6 },
month = { Sep },
year = { 2017 },
issn = { 0975-8887 },
pages = { 16-19 },
numpages = {9},
url = { },
doi = { 10.5120/ijca2017915415 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
%0 Journal Article
%1 2024-02-07T00:23:45.526391+05:30
%A Sara I. Basheer
%A Younis M. Abbosh
%A Majid Dh. Younis
%T Pre-Processing Algorithms on Digital Mammograms
%J International Journal of Computer Applications
%@ 0975-8887
%V 174
%N 6
%P 16-19
%D 2017
%I Foundation of Computer Science (FCS), NY, USA

Breast cancer is the greatest challenging health complexities that medical science is facing. Presently, there are no active methods to avert breast cancer, because its cause is not yet completely identified. Screening mammography is the available method that is currently used for reliable detection of breast cancer. Computer Aided diagnosis (CAD) techniques are used to enhance the diagnostic accuracy and efficiency of screening mammography. The sensitivity of mammogram decreases due to some factors like density of breast, presence of labels, and artifacts or even pectoral muscle. Therefore, the preprocessing of mammograms is a very significant step in breast cancer analysis and detection since it might reduce the number of false positive. In this paper, several procedures have been performed for preprocessing including (noise reduction, separate the breast from the artifacts and pectoral muscle, mammogram alignment).

  1. Parkin DM et al. Global cancer statistics 2002, CA: A Cancer Journal for Clinicians, 2005,55:74–108.
  2. Parkin DM, Fernandez LM. Use of statistics to assess the global burden of breast cancer. Breast,2006, 12(1 Suppl.):S70–S80.
  3. J. Ferlay, F. Bray, P. Pisani and D.M. Parkin. GLOBOCAN 2000: Cancer Incidence, Mortality and PrevalenceWorldwide. Version 1.0. IARC CancerBase No. 5. Lyon, IARC Press, 2001.
  4. Rangayyan, Rangaraj M., Fabio J. Ayres, and J. E. Leo Desautels. "A review of computer-aided diagnosis of breast cancer: Toward the detection of subtle signs." Journal of the Franklin Institute 344.3 (2007): 312-348.‏
  5. Mirzaalian, Hengameh, et al. "Pre-processing Algorithms on Digital Mammograms." MVA. 2007.‏
  6. M.L. Giger, R.M. Nishikawa, M. Kupinski, U. Bick, M. Zhang, R.A. Schmidt, D.E. Wolverton, C.E. Comstock, J. Papaioannou, S.A. Collins, A.M. Urbas, C.J. Vyborny, and K. Doi, “Computerized Detection of Breast Lesions in Digitized Mammograms and Results with a Clinically-Implemented Intelligent Workstation”, presented at Computer AssistedRadiology and Surgery, Berlin, Germany, pp.325-330, 1997.
  7. M. A, Wirth, Nonrigid Approach to Medical Image Registration: Matching Images of the Breast, Ph.D. Thesis, RMIT University, Melbourne, Australia, 2000.
  8. R. M. Haralick and L. G. Shapiro, “Image segmentation techniques,” Comput. Vis. Graph. Image Process., vol. 29, pp. 100-132, 1985.
  9. F. Georgsson, "Differential analysis of bilateral mammograms", International Journal on Pattern Recognition and Artificial Intelligence, Vol. 17, July 2003, pp. 1207-1226.
  10. T. K. Lau and W. F. Bischof, “Automated detection of breast tumors using the asymmetry approach,” Comput. Biomed. Res., vol. 24, pp.273–295, 1991.
  11. Bick, U., Giger, M.: Automated segmentation of digitized mammograms. In: Academic Radiology. Volume 2. (1995) 1-9.
  12. Maitra, IndraKanta, Sanjay Nag, and Samir Kumar Bandyopadhyay. "Bilateral Breast Asymmetry Detection using Intensity Histogram and Landmark based Registration Technique." International Journal of Emerging Sciences 2.3 (2012).‏
  13. J.Suckling et al, "The Mammographic Image Analysis Society Digital Mammogram Database", ExerptaMedica, 1994, Vol.1069, pp.375 – 378.
  14. Indra Kanta Maitra et al, “Artifact Suppression and Homogenous Orientation of Digital Mammogram using Seeded Region Growing Algorithm”, International Journal of Computer Information Systems, 2011, Vol. 3, No. 4, pp.32-38.
  15. Bandyopadhyay, Samir Kumar. "Preprocessing of Mammogram Images." International Journal of Engineering Science and Technology 2.11 (2010): 6753-6758.‏
Index Terms

Computer Science
Information Sciences


Digital Mammogram Breast Cancer Preprocessing