Breast cancer is the second leading cause of death for women all over the world. Screening mammography is currently the best available radiological technique for early detection of breast cancer. However the presence of artifacts can disturb the detection of breast cancer and reduce the rate of accuracy in the computer aided detection (CAD) systems. For this reason, the pre-processing of mammogram images is very important in the process of breast cancer analysis because it reduces the number of false positive. This paper proposes various filtering techniques to solve the noise removal problems and separate the background region from the breast profile region using an automatic thresholding technique and Connected Component Labelling. We evaluated our pre-processing method on a set of images obtained from a private hospital. Thus this preparation phase improves the image quality and accentuates the CAD results more accurate.

1. Breast Cancer Facts and Figures 2009-2010, ACS.
2. Gøtzsche PC, Nielsen M (2011). "Screening for breast cancer with mammography". Cochrane Database Syst Rev (Online): Available. doi:10. 1002/14651858. CD001877. pub4.
3. K. H. Ng and M. Muttarak, "Advances in mammography have improved early detection of breast cancer," J. Hong Kong College Radiol. , vol. 6, no. 3, pp. 126131, 2003.
4. Jinshan Tang, Senior Member, IEEE, Rangaraj M. Rangayyan, Fellow, IEEE, Jun Xu, Issam El Naqa, Member, IEEE, and Yongyi Yang, Senior Member, IEEE "Computer-Aided Detection and Diagnosis of Breast Cancer with Mammography: Recent Advances", IEEE Transaction on Information Technology on Biomedicine, VOL. 13, NO. 2 March 2009.
5. Samir Kumar Bandyopadhyay, pre-processing of Mammogram Images, International Journal of Engineering Science and Technology, Vol. 2(11), 2010, 6753-6758.
6. R. Chandrasekhar, and Y. Attikiouzel, "Automatic Breast Border Segmentation by Background Modelling and Subtraction", in 5th International Workshop on Digital Mammography (IWDM), (Yaffe M. ed. ), Medical Physics Publishing, Madison, USA, pp. 560–565.
7. M. Kass, A. Witkin, and D. Terzopoulos, "Snakes: Active Contour Models", International Journal of Computer Vision, vol. 1, pp. 321–331, 1998.
8. U. Bick, M. L. Giger, R. A. Schmidt, R. M. Nishikawa, D. E. Wolverton, and K. Doi, "Automated Segmentation of Digitized Mammograms", Academic Radiology, vol. 2, no. 2, pp. 1–9, 1995.
9. A. J. Mendez, P. J. Tahoces, M. J. Lado, M. Souto, J. L. Correa, and J. J. Vidal, "Automatic Detection of Breast Border and Nipple in Digital Mammograms", Computer Methods and Programs in Biomedicine, vol. 49, pp. 253– 262, 1996.
10. Gordon AB, Fisher C, Palmer B, Greening WP. Granular cell tumour of the breast. Eur J Surg Oncol. 1985; 11:269–73. [PubMed].
11. Anish Patel, Valentina Lefemine, Syed Mansoor Yousuf, and Walid Abou-Samra, Granular cell tumour of the pectoral muscle mimicking breast cancer, Eur J Surg Oncol. 1985; 11:269–73. [PubMed].
12. D. Spurgeon, "Digital mammography is more accurate only for certain groups of women," Br. Med. J. , vol. 331, no. 7518, pp. 653–653, 2005.
13. E. D. Pisano, C. Gatsonis, E. Hendrick, M. Yaffe, J. Baum, S. Acharyya, E. Conant, L. Fajardo, L. Bassett, C. D'Orsi, R. Jong, and M. Rebner, "Diagnostic performance of digital versus film mammography for breast cancer screening," New England J. Med. , vol. 353, no. 17, pp. 1773–1783, 2005.
14. N. Otsu, "A threshold selection method from grey level histogram", IEEE Trans. Syst. Man Cybern. , vol. 9 no. 1, 1979, pp. 62-66.
15. He J, Q. D. M. Do, A. C. Downtown and J. H. Kim, A Comparison of Binarization Methods for Historical Archive Documents, Proc of the 8th International Conf on Document Analysis and Recognition (ICDAR'05) 2005 IEEE.
16. J. Kittler and J. Illingworth. Minimum error thresholding. Pattern Recognition, 19:41. 47, 1986.
17. J. Bozek, M. Mustra, K. Delac, M. Grgic, "A Survey of Image Processing Algorithms in Digital Mammography", Springer-Verlag, pp. 631–657, Berlin Heidelberg, 2009.
18. R. J. Ferrari, A. F. Frere, R. M. Rangayyan, J. E. L. Desautels, R. A. Borges, "Identification of the breast boundary in mammograms using active contour models", In Med. Biol. Eng. Comput. , vol. 42, pp. 201-208, 2004.
19. H. Mirzaalian, M. R Ahmadzadeh, S. Sadri, M. Jafari, "Pre-processing Algorithms on Digital Mammograms", In MVA2007 IAPR Conference on Machine Vision Applications, pp. 118-121, 2007.

Mammogram Thresholding Connected Component Labelling Breast Region Extraction Computer Aided Analysis Otsu Method Kittler Method Wiener Filter