title: A population-based tissue probability map-driven level set method for fully automated mammographic density estimations.
abstract: OBJECTIVE
  A major challenge when distinguishing glandular tissues on mammograms, especially for area-based estimations, lies in determining a boundary on a hazy transition zone from adipose to glandular tissues. This stems from the nature of mammography, which is a projection of superimposed tissues consisting of different structures. In this paper, the authors present a novel segmentation scheme which incorporates the learned prior knowledge of experts into a level set framework for fully automated mammographic density estimations.
  
  
  METHODS
  The authors modeled the learned knowledge as a population-based tissue probability map (PTPM) that was designed to capture the classification of experts' visual systems. The PTPM was constructed using an image database of a selected population consisting of 297 cases. Three mammogram experts extracted regions for dense and fatty tissues on digital mammograms, which was an independent subset used to create a tissue probability map for each ROI based on its local statistics. This tissue class probability was taken as a prior in the Bayesian formulation and was incorporated into a level set framework as an additional term to control the evolution and followed the energy surface designed to reflect experts' knowledge as well as the regional statistics inside and outside of the evolving contour.
  
  
  RESULTS
  A subset of 100 digital mammograms, which was not used in constructing the PTPM, was used to validate the performance. The energy was minimized when the initial contour reached the boundary of the dense and fatty tissues, as defined by experts. The correlation coefficient between mammographic density measurements made by experts and measurements by the proposed method was 0.93, while that with the conventional level set was 0.47.
  
  
  CONCLUSIONS
  The proposed method showed a marked improvement over the conventional level set method in terms of accuracy and reliability. This result suggests that the proposed method successfully incorporated the learned knowledge of the experts' visual systems and has potential to be used as an automated and quantitative tool for estimations of mammographic breast density levels.
authors.affiliation: Interdisciplinary Program of Radiation Applied Life Science, Seoul National University College of Medicine, Seoul, South Korea 110-744 and Center for Medical-IT Convergence Technology Research, Advanced Institutes of Convergence Technology, Suwon, South Korea 443-270.
authors.forename: Youngwoo
authors.initials: Y
authors.lastname: Kim
authors.affiliation: School of Computer Science and Engineering, Chung-Ang University, Seoul, South Korea 156-756.
authors.forename: Byung Woo
authors.initials: BW
authors.lastname: Hong
authors.affiliation: Department of Radiology, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, South Korea 156-756.
authors.forename: Seung Ja
authors.initials: SJ
authors.lastname: Kim
authors.affiliation: Center for Medical-IT Convergence Technology Research, Advanced Institutes of Convergence Technology, Suwon, South Korea 443-270; Department of Radiology, Institute of Radiation Medicine, Seoul National University College of Medicine, 28, Yongon-dong, Chongno-gu, Seoul, 110-744, Korea; and Department of Transdisciplinary Studies, Graduate School of Convergence Science and Technology, Seoul National University, Seoul, South Korea 110-744.
authors.forename: Jong Hyo
authors.initials: JH
authors.lastname: Kim
mesh_terms: D000273:Adipose Tissue / Q000000981:diagnostic imaging; D000328:Adult; D000368:Aged; D000465:Algorithms; D001499:Bayes Theorem; D000071060:Breast Density; D001943:Breast Neoplasms; D016208:Databases, Factual; D005260:Female; D006801:Humans; D042361:Mammary Glands, Human / Q000002:abnormalities; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods*; D011336:Probability; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results