title: Volumetric breast density estimation from full-field digital mammograms.
abstract: A method is presented for estimation of dense breast tissue volume from mammograms obtained with full-field digital mammography (FFDM). The thickness of dense tissue mapping to a pixel is determined by using a physical model of image acquisition. This model is based on the assumption that the breast is composed of two types of tissue, fat and parenchyma. Effective linear attenuation coefficients of these tissues are derived from empirical data as a function of tube voltage (kVp), anode material, filtration, and compressed breast thickness. By employing these, tissue composition at a given pixel is computed after performing breast thickness compensation, using a reference value for fatty tissue determined by the maximum pixel value in the breast tissue projection. Validation has been performed using 22 FFDM cases acquired with a GE Senographe 2000D by comparing the volume estimates with volumes obtained by semi-automatic segmentation of breast magnetic resonance imaging (MRI) data. The correlation between MRI and mammography volumes was 0.94 on a per image basis and 0.97 on a per patient basis. Using the dense tissue volumes from MRI data as the gold standard, the average relative error of the volume estimates was 13.6%.
authors.affiliation: Radboud University Nijmegen Medical Centre, Department of Radiology, The Netherlands. S.vanEngeland@rad.umcn.nl
authors.forename: Saskia
authors.initials: S
authors.lastname: van Engeland
authors.forename: Peter R
authors.initials: PR
authors.lastname: Snoeren
authors.forename: Henkjan
authors.initials: H
authors.lastname: Huisman
authors.forename: Carla
authors.initials: C
authors.lastname: Boetes
authors.forename: Nico
authors.initials: N
authors.lastname: Karssemeijer
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000175:diagnosis*; D003720:Densitometry / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008327:Mammography / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted