2013/10/01 by Mario Muštra, Muštra, Mario, Mislav Grgić +1
Computer Science · Medicine · #AI in cancer detection #Computer Vision and Pattern Recognition (cs.CV) #Digital Radiography and Breast Imaging #FOS: Computer and information sciences #Infrared Thermography in Medicine #cs.CV
paper · pdf · doi:10.48550/arxiv.1310.0305
Part of the Proceedings of the Croatian Computer Vision Workshop, CCVW 2013, Year 1
arxiv created 2013/10/01 · openalex publication_date 2013/10/01 · arxiv updated 2013/10/02 · openalex created_date 2023/03/19 · openalex updated_date 2026/07/28
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.