  Breast tissue segmentation into dense and fat tissue is important for
determining the breast density in mammograms. Knowing the breast density is
important both in diagnostic and computer-aided detection applications. There
are many different ways to express the density of a breast and good quality
segmentation should provide the possibility to perform accurate classification
no matter which classification rule is being used. Knowing the right breast
density and having the knowledge of changes in the breast density could give a
hint of a process which started to happen within a patient. Mammograms
generally suffer from a problem of different tissue overlapping which results
in the possibility of inaccurate detection of tissue types. Fibroglandular
tissue presents rather high attenuation of X-rays and is visible as brighter in
the resulting image but overlapping fibrous tissue and blood vessels could
easily be replaced with fibroglandular tissue in automatic segmentation
algorithms. Small blood vessels and microcalcifications are also shown as
bright objects with similar intensities as dense tissue but do have some
properties which makes possible to suppress them from the final results. In
this paper we try to divide dense and fat tissue by suppressing the scattered
structures which do not represent glandular or dense tissue in order to divide
mammograms more accurately in the two major tissue types. For suppressing blood
vessels and microcalcifications we have used Gabor filters of different size
and orientation and a combination of morphological operations on filtered image
with enhanced contrast.
