PristupačnostFiltering for More Accurate Dense Tissue Segmentation in Digitized Mammograms
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 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.
Files
DOI
10.20532/ccvw.2013.0015
https://doi.org/10.20532/ccvw.2013.0015
BibTeX
@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}
}