@InProceedings{10.20532/ccvw.2013.0015,
  author =       {Mario Mu{\v s}tra and Mislav Grgi{\' c}},
  title =        {Filtering for More Accurate Dense Tissue
                  Segmentation in Digitized Mammograms},
  booktitle =    {Proceedings of the Croatian Compter Vision Workshop,
                  Year 1},
  pages =        {53-57},
  year =         2013,
  editor =       {Lon{\v c}ari{\' c}, Sven and {\v S}egvi{\' c},
                  Sini{\v s}a},
  address =      {Zagreb},
  month =        {September},
  organization = {Center of Excellence for Computer Vision},
  publisher =    {University of Zagreb},
  abstract =     {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 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 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
                  brighter 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 scattered structures which do not
                  represent glandular or dense tissue in order to
                  divide mammograms more accurately in 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.},
  doi =          {10.20532/ccvw.2013.0015},
  url =          {https://doi.org/10.20532/ccvw.2013.0015}
}