title: Dynamic multiple thresholding breast boundary detection algorithm for mammograms.
abstract: OBJECTIVE
  Automated detection of breast boundary is one of the fundamental steps for computer-aided analysis of mammograms. In this study, the authors developed a new dynamic multiple thresholding based breast boundary (MTBB) detection method for digitized mammograms.
  
  
  METHODS
  A large data set of 716 screen-film mammograms (442 CC view and 274 MLO view) obtained from consecutive cases of an Institutional Review Board approved project were used. An experienced breast radiologist manually traced the breast boundary on each digitized image using a graphical interface to provide a reference standard. The initial breast boundary (MTBB-Initial) was obtained by dynamically adapting the threshold to the gray level range in local regions of the breast periphery. The initial breast boundary was then refined by using gradient information from horizontal and vertical Sobel filtering to obtain the final breast boundary (MTBB-Final). The accuracy of the breast boundary detection algorithm was evaluated by comparison with the reference standard using three performance metrics: The Hausdorff distance (HDist), the average minimum Euclidean distance (AMinDist), and the area overlap measure (AOM).
  
  
  RESULTS
  In comparison with the authors' previously developed gradient-based breast boundary (GBB) algorithm, it was found that 68%, 85%, and 94% of images had HDist errors less than 6 pixels (4.8 mm) for GBB, MTBB-Initial, and MTBB-Final, respectively. 89%, 90%, and 96% of images had AMinDist errors less than 1.5 pixels (1.2 mm) for GBB, MTBB-Initial, and MTBB-Final, respectively. 96%, 98%, and 99% of images had AOM values larger than 0.9 for GBB, MTBB-Initial, and MTBB-Final, respectively. The improvement by the MTBB-Final method was statistically significant for all the evaluation measures by the Wilcoxon signed rank test (p < 0.0001).
  
  
  CONCLUSIONS
  The MTBB approach that combined dynamic multiple thresholding and gradient information provided better performance than the breast boundary detection algorithm that mainly used gradient information.
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor Michigan 48109, USA. yitawu@itri.org.tw
authors.forename: Yi-Ta
authors.initials: YT
authors.lastname: Wu
authors.forename: Chuan
authors.initials: C
authors.lastname: Zhou
authors.forename: Heang-Ping
authors.initials: HP
authors.lastname: Chan
authors.forename: Chintana
authors.initials: C
authors.lastname: Paramagul
authors.forename: Lubomir M
authors.initials: LM
authors.lastname: Hadjiiski
authors.forename: Caroline Plowden
authors.initials: CP
authors.lastname: Daly
authors.forename: Julie A
authors.initials: JA
authors.lastname: Douglas
authors.forename: Yiheng
authors.initials: Y
authors.lastname: Zhang
authors.forename: Berkman
authors.initials: B
authors.lastname: Sahiner
authors.forename: Jiazheng
authors.initials: J
authors.lastname: Shi
authors.forename: Jun
authors.initials: J
authors.lastname: Wei
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods*; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity