title: Characterization of masses in digital breast tomosynthesis: comparison of machine learning in projection views and reconstructed slices.
abstract: OBJECTIVE
  In digital breast tomosynthesis (DBT), quasi-three-dimensional (3D) structural information is reconstructed from a small number of 2D projection view (PV) mammograms acquired over a limited angular range. The authors developed preliminary computer-aided diagnosis (CADx) methods for classification of malignant and benign masses and compared the effectiveness of analyzing lesion characteristics in the reconstructed DBT slices and in the PVs.
  
  
  METHODS
  A data set of MLO view DBT of 99 patients containing 107 masses (56 malignant and 51 benign) was collected at the Massachusetts General Hospital with IRB approval. The DBTs were obtained with a GE prototype system which acquired 11 PVs over a 50 degree arc. The authors reconstructed the DBTs at 1 mm slice interval using a simultaneous algebraic reconstruction technique. The region of interest (ROI) containing the mass was marked by a radiologist in the DBT volume and the corresponding ROIs on the PVs were derived based on the imaging geometry. The subsequent processes were fully automated. For classification of masses using the DBT-slice approach, the mass on each slice was segmented by an active contour model initialized with adaptive k-means clustering. A spiculation likelihood map was generated by analysis of the gradient directions around the mass margin and spiculation features were extracted from the map. The rubber band straightening transform (RBST) was applied to a band of pixels around the segmented mass boundary. The RBST image was enhanced by Sobel filtering in the horizontal and vertical directions, from which run-length statistics texture features were extracted. Morphological features including those from the normalized radial length were designed to describe the mass shape. A feature space composed of the spiculation features, texture features, and morphological features extracted from the central slice alone and seven feature spaces obtained by averaging the corresponding features from three to 19 slices centered at the central slice were compared. For classification of masses using the PV approach, a feature extraction process similar to that described above for the DBT approach was performed on the ROIs from the individual PVs. Six feature spaces obtained from the central PV alone and by averaging the corresponding features from three to 11 PVs were formed. In each feature space for either the DBT-slice or the PV approach, a linear discriminant analysis classifier with stepwise feature selection was trained and tested using a two-loop leave-one-case-out resampling procedure. Simplex optimization was used to guide feature selection automatically within the training set in each leave-one-case-out cycle. The performance of the classifiers was evaluated by the area (Az) under the receiver operating characteristic curve.
  
  
  RESULTS
  The test Az values from the DBT-slice approach ranged from 0.87 +/- 0.03 to 0.93 +/- 0.02, while those from the PV approach ranged from 0.78 +/- 0.04 to 0.84 +/- 0.04. The highest test Az of 0.93 +/- 0.02 from the nine-DBT-slice feature space was significantly (p = 0.006) better than the highest test Az of 0.84 +/- 0.04 from the nine-PV feature space.
  
  
  CONCLUSIONS
  The features of breast lesions extracted from the DBT slices consistently provided higher classification accuracy than those extracted from the PV images.
authors.affiliation: Department of Radiology, University of Michigan, Ann Arbor, Michigan 48109, USA. chanhp@umich.edu
authors.forename: Heang-Ping
authors.initials: HP
authors.lastname: Chan
authors.forename: Yi-Ta
authors.initials: YT
authors.lastname: Wu
authors.forename: Berkman
authors.initials: B
authors.lastname: Sahiner
authors.forename: Jun
authors.initials: J
authors.lastname: Wei
authors.forename: Mark A
authors.initials: MA
authors.lastname: Helvie
authors.forename: Yiheng
authors.initials: Y
authors.lastname: Zhang
authors.forename: Richard H
authors.initials: RH
authors.lastname: Moore
authors.forename: Daniel B
authors.initials: DB
authors.lastname: Kopans
authors.forename: Lubomir
authors.initials: L
authors.lastname: Hadjiiski
authors.forename: Ted
authors.initials: T
authors.lastname: Way
mesh_terms: D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging; D016002:Discriminant Analysis; D006801:Humans; D008327:Mammography; D012372:ROC Curve; D011856:Radiographic Image Enhancement; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*