title: Image segmentation in digital mammography: comparison of local thresholding and region growing algorithms.
abstract: Local thresholding and region-growing algorithms are developed and applied to digitized mammograms to quantify the parenchymal densities. The algorithms are first evaluated and optimized on phantom images reflecting varying image contrast, X-ray exposure conditions, and time-related changes. The difference between the segmentation results of the two techniques is less than 6% on the phantom images and 11% on the mammograms. The agreement between the computerized procedures and a manual one is in the range of 74-98%, depending on the breast parenchymal pattern and segmentation algorithm. The results show that computerized parenchymal classification of digitized mammograms is possible and independent of exposure.
authors.affiliation: Center for Engineering and Medical Image Analysis (CEMIA), College of Engineering, University of South Florida, Tampa 33612-4799.
authors.forename: M
authors.initials: M
authors.lastname: Kallergi
authors.forename: K
authors.initials: K
authors.lastname: Woods
authors.forename: L P
authors.initials: LP
authors.lastname: Clarke
authors.forename: W
authors.initials: W
authors.lastname: Qian
authors.forename: R A
authors.initials: RA
authors.lastname: Clark
mesh_terms: D000465:Algorithms; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008327:Mammography / Q000379:methods*; D008961:Models, Structural; D015203:Reproducibility of Results