  Mammography is a vital screening technique for early revealing and
identification of breast cancer in order to assist to decrease mortality rate.
Practical applications of mammograms are not limited to breast cancer
revealing, identification ,but include task based lens design, image
compression, image classification, content based image retrieval and a host of
others. Mammography computational analysis methods are a useful tool for
specialists to reveal hidden features and extract significant information in
mammograms. Digital mammograms are mammography images available along with the
conventional screen-film mammography to make automation of mammograms easier.
In this paper, we descriptively discuss computational advancement in digital
mammograms to serve as a compass for research and practice in the domain of
computational mammography and related fields. The discussion focuses on
research aiming at a variety of applications and automations of mammograms. It
covers different perspectives on image pre-processing, feature extraction,
application of mammograms, screen-film mammogram, digital mammogram and
development of benchmark corpora for experimenting with digital mammograms.
