Dense tissue segmentation in digitized mammograms

Determining the breast density in mammograms is important both in diagnostic and computer-aided detection applications. Knowing the right breast density and having knowledge of changes in breast density could give a hint of a process which started to happen within a patient. Breast density could be rather easily estimated by dividing mammogram into fibroglandular and fat tissue. Mammograms suffer from a problem of overlapping tissue which results in possibility of inaccurate detection of tissue types. Fibroglandular tissue has rather high attenuation of X-rays and is visible as brighter in the resulting image. Small blood vessels and microcalcifications are shown as brighter objects with similar intensities as dense tissue. 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 we have used Gabor filters of different size and orientation to detect edges of blood vessels and subtract them from the original image. Microcalcifications have been suppressed by combination of morphological operations on filtered image with enhanced contrast. Dense tissue has been segmented using different thresholds to avoid false detection.