title: Multilevel learning-based segmentation of ill-defined and spiculated masses in mammograms.
abstract: OBJECTIVE
  A learning-based approach integrating the use of pixel-level statistical modeling and spiculation detection is presented for the segmentation of mammographic masses with ill-defined margins and spiculations.
  
  
  METHODS
  The algorithm involves a multiphase pixel-level classification, using a comprehensive group of features computed from regional intensity, shape, and textures, to generate a mass-conditional probability map (PM). Then, the mass candidate, along with the background clutters consisting of breast fibroglandular and other nonmass tissues, is extracted from the PM by integrating the prior knowledge of shape and location of masses. A multiscale steerable ridge detection algorithm is employed to detect spiculations. Finally, all the object-level findings, including mass candidate, detected spiculations, and clutters, along with the PM, are integrated by graph cuts to generate the final segmentation mask.
  
  
  RESULTS
  The method was tested on 54 masses (51 malignant and 3 benign), all with ill-defined margins and irregular shape or spiculations. The ground truth delineations were provided by five experienced radiologists. Area overlapping ratio of 0.689 (+/- 0.160) and 0.540 (+/- 0.164) were obtained for segmenting entire mass and margin portion only, respectively. Williams index of area and contour based measurements indicated that the segmentation results of the algorithm agreed well with the radiologists' delineation.
  
  
  CONCLUSIONS
  The proposed approach could closely delineate the mass body. Most importantly, it is capable of including mass margin and its spicule extensions which are considered as key features for breast lesion analyses.
authors.affiliation: Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Arlington, Virginia 22203, USA. ymtao@vt.edu
authors.forename: Yimo
authors.initials: Y
authors.lastname: Tao
authors.forename: Shih-Chung B
authors.initials: SC
authors.lastname: Lo
authors.forename: Matthew T
authors.initials: MT
authors.lastname: Freedman
authors.forename: Erini
authors.initials: E
authors.lastname: Makariou
authors.forename: Jianhua
authors.initials: J
authors.lastname: Xuan
mesh_terms: D000465:Algorithms; D001940:Breast / Q000473:pathology; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D003196:Computer Graphics; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008327:Mammography / Q000379:methods*; D015233:Models, Statistical; D015588:Observer Variation; D011871:Radiology / Q000379:methods; D015203:Reproducibility of Results