title: Improving mass candidate detection in mammograms via feature maxima propagation and local feature selection.
abstract: OBJECTIVE
  Mass candidate detection is a crucial component of multistep computer-aided detection (CAD) systems. It is usually performed by combining several local features by means of a classifier. When these features are processed on a per-image-location basis (e.g., for each pixel), mismatching problems may arise while constructing feature vectors for classification, which is especially true when the behavior expected from the evaluated features is a peaked response due to the presence of a mass. In this study, two of these problems, consisting of maxima misalignment and differences of maxima spread, are identified and two solutions are proposed.
  
  
  METHODS
  The first proposed method, feature maxima propagation, reproduces feature maxima through their neighboring locations. The second method, local feature selection, combines different subsets of features for different feature vectors associated with image locations. Both methods are applied independently and together.
  
  
  RESULTS
  The proposed methods are included in a mammogram-based CAD system intended for mass detection in screening. Experiments are carried out with a database of 382 digital cases. Sensitivity is assessed at two sets of operating points. The first one is the interval of 3.5-15 false positives per image (FPs/image), which is typical for mass candidate detection. The second one is 1 FP/image, which allows to estimate the quality of the mass candidate detector's output for use in subsequent steps of the CAD system. The best results are obtained when the proposed methods are applied together. In that case, the mean sensitivity in the interval of 3.5-15 FPs/image significantly increases from 0.926 to 0.958 (p < 0.0002). At the lower rate of 1 FP/image, the mean sensitivity improves from 0.628 to 0.734 (p < 0.0002).
  
  
  CONCLUSIONS
  Given the improved detection performance, the authors believe that the strategies proposed in this paper can render mass candidate detection approaches based on image location classification more robust to feature discrepancies and prove advantageous not only at the candidate detection level, but also at subsequent steps of a CAD system.
authors.affiliation: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, 6525 GA, The Netherlands.
authors.forename: Jaime
authors.initials: J
authors.lastname: Melendez
authors.affiliation: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, 6525 GA, The Netherlands.
authors.forename: Clara I
authors.initials: CI
authors.lastname: Sánchez
authors.affiliation: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, 6525 GA, The Netherlands.
authors.forename: Bram
authors.initials: B
authors.lastname: van Ginneken
authors.affiliation: Diagnostic Image Analysis Group, Department of Radiology and Nuclear Medicine, Radboud University Medical Center, Nijmegen, 6525 GA, The Netherlands.
authors.forename: Nico
authors.initials: N
authors.lastname: Karssemeijer
mesh_terms: D000368:Aged; D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods*; D008875:Middle Aged; D016571:Neural Networks, Computer; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012680:Sensitivity and Specificity