Breast cancer is ranked second among the leading causes of death affecting females. Statistics have shown that one out of eight (12 %) women are affected by breast cancer in their lifetime. Mammography is the most effective strategy for breast cancer screening and can be used for the early detection of masses or abnormalities. Small clusters of micro calcifications appearing as a collection of white spots on mammograms show an early sign of breast cancer. In digital mammography, electronic image of the breast is taken and is stored directly in a computer. However, early detection of breast cancer is dependent on both the radiologist's ability to read mammograms and the quality of mammogram images. The aim of this paper is to conduct a review of existing mammogram enhancement techniques. Each method will be discussed in brief and compared against other approaches.

1. Schiabel, Santos, Angelo, "Segmentation Technique for Detecting Suspect Masses in Dense Breast Digitized Images as a Tool for Mammography CAD Schemes," In Proc. of the 2008 ACM Symp. On Appl. Computers. , 2008.
2. Sampat, Bovik, "Detection of Speculated Lesions in Mammograms," In Proc. of the 25th Annual Int. IEEE Conference on Eng. in Medicine and Biological Soc. , pp. 810-813, 2008.
3. Dabour,W, "Improved Wavelet Based Thresholding for Contrast Enhancement of Digital Mammograms," In Proc. of the 2008 Int. Conf. on Comput. Sci. and Softw. Eng. 2008.
4. Mohanalin, Kalra, P. K. , Kumar, N ,"An Automatic Method to Enhance Microcalcifications using Normalized Tsallisen Tropy," Signal Process. ,90, pp952-958, 2010.
5. Jiang, J. , Yao, B. , Wason, A. M. , "Integration of Fuzzy Logic and Structure Tensor towards Mammogram Contrast Enhancement", Comput. Med. Imaging Graphics. 29, pp83-90, 2005.
6. M. Sundaram, K. Ramar, N. Arumugam, "Histogram Based Contrast Enhancement for Mammogram Images," In Proceedings International Conference on Signal Processing, Communication, Computing and Networking Technologies, 2011.
7. Peyman Rahmati, Ghassan Hamarneh, Doron Nussbaum, and Andy Adler, "A New Preprocessing Filter for Digital Mammograms," Dept. of System and computer Engineering, Carleton University, ON, Canada, 2012.
8. Mariam Biltawi, Nijad al Najdawi, Sara Tedmori, "Mammogram Enhancement and Segmentation methods: Classification, Analysis andEvaluation, "The 13th International Arab conference on information Technology, 2012.
9. Ojala, T. , Näppi, J. , Nevalainen, O. , "Accurate Segmentation of the Breast Region from Digitized Mammograms," Computer Med. imaging graphics: the official Journal of the Comput. Med. Imaging Society 25, pp. 47-59, 2001.
10. Wang, K. ,Qin, H. , Fisher, P. R. , Zhao, "Automatic Registration of Mammograms using Texture-Based Anisotropic Features". In: Proc. of 2006 IEEE Int. Symp. On Biomed. Imaging: From Nano to Macro, pp. 864-867, 2006.
11. Raba, D. , Oliver, A. , Marti, J. , Peracaula, M. , Espunya, J. , "Breast Segmentation with Pectoral Muscle Suppression on Digital Mammograms. " In: Iber. Conf. on Pattern Recognition. And Image Anal. , pp471-478, 2005.
12. Bajger, M. , Ma, F. , Bottema, M. J. , "Automatic Tuning of MST Segmentation of Mammograms for Registration and Mass Detection Algorithms". Digit. Image Comput. Pp400-407, 2009
13. S. Saheb Basha, DR. K. Satya Prasad, "Automatic Detection of Breast Cancer Mass in Mammograms using Morphological operators and Fuzzy C –Means clustering", Journal of Theoretical and Applied Information Technology, 2009.
14. Harish Kumar. N, Amutha. S, Dr. Ramesh Babu . D. R, "Enhancement of Mammographic Images using Morphology and Wavelet Transform", Int. J. Computer Technology and Applications. 2013, Vol 3,192-198.

Mammogram Enhancement Image Calcification Breast Mass Detection Segmentation Microcalcification Detection Morphology Wavelet Transform.