title: Identification of breast contour for nipple segmentation in breast magnetic resonance images.
abstract: OBJECTIVE
  The purpose of this study is to develop a method to simulate the breast contour and segment the nipple in breast magnetic resonance images.
  
  
  METHODS
  This study first identifies the chest wall and removes the chest part from the breast MR images. Subsequently, the cleavage and its motion artifacts are removed, distinguishing the separate breasts, where the edge points are sampled for curve fitting. Next, a region growing method is applied to find the potential nipple region. Finally, the potential nipple region above the simulated curve can be removed in order to retain the original smooth contour.
  
  
  RESULTS
  The simulation methods can achieve the least root mean square error (RMSE) for certain cases. The proposed YBnd and (Dmin+Dmax)/2 methods are significant due toP = 0.000. The breast contour curve detected by the two proposed methods is closer than that determined by the edge detection method. The (Dmin+Dmax)/2 method can achieve the lowest RMSE of 1.1029 on average, while the edge detection method results in the highest RMSE of 6.5655. This is only slighter better than the comparison methods, which implies that the performance of these methods depends upon the conditions of the cases themselves. Under this method, the maximal Dice coefficient is 0.881, and the centroid difference is 0.36 pixels.
  
  
  CONCLUSIONS
  The contributions of this study are twofold. First, a method was proposed to identify and segment the nipple in breast MR images. Second, a curve-fitting method was used to simulate the breast contour, allowing the breast to retain its original smooth shape.
authors: Department of Information Management, Chien Hsin University of Science and Technology, Taoyuan 320, Taiwan.
authors: Chih-Ying
authors: CY
authors: Gwo
authors: Department of Chemistry and Biochemistry, University of Texas at Austin, Austin, Texas 78712.
authors: Allen
authors: A
authors: Gwo
authors: Department of Information Management, Chien Hsin University of Science and Technology, Taoyuan 320, Taiwan and Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei 110, Taiwan.
authors: Chia-Hung
authors: CH
authors: Wei
authors: Graduate Institute of Biomedical Informatics, Taipei Medical University, Taipei 110, Taiwan and Comprehensive Breast Health Center, Taipei Medical University Hospital, Taipei 110, Taiwan.
authors: Pai Jung
authors: PJ
authors: Huang
mesh_terms: D001940:Breast / Q000033:anatomy & histology*; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009558:Nipples / Q000033:anatomy & histology*