title: Evaluating the effect of image preprocessing on an information-theoretic CAD system in mammography.
abstract: OBJECTIVE
  In our earlier studies, we reported an evidence-based computer-assisted decision (CAD) system for location-specific interrogation of mammograms. A content-based image retrieval framework with information theoretic (IT) similarity measures serves as the foundation for this system. Specifically, the normalized mutual information (NMI) was shown to be the most effective similarity measure for reduction of false-positive marks generated by other prescreening mass detection schemes. The objective of this work was to investigate the importance of image filtering as a possible preprocessing step in our IT-CAD system.
  
  
  METHODS
  Different filters were applied, each one aiming to compensate for known limitations of the NMI similarity measure. The study was based on a region-of-interest database that included true masses and false-positive regions from digitized mammograms.
  
  
  RESULTS
  Receiver-operating characteristics (ROC) analysis showed that IT-CAD is affected slightly by image filtering. Modest, yet statistically significant, performance gain was observed with median filtering (overall ROC area index A(z) improved from 0.78 to 0.82). However, Gabor filtering improved performance for the high-sensitivity portion of the ROC curve where a typical false-positive reduction scheme should operate (partial ROC area index (0.90)A(z) improved from 0.33 to 0.37). Fusion of IT-CAD decisions from different filtering schemes markedly improved performance (A(z) = 0.90 and (0.90)A(z) = 0.55). At 95% sensitivity, the system's specificity improved by 36.6%.
  
  
  CONCLUSIONS
  Additional improvement in false-positive reduction can be achieved by incorporating image filtering as a preprocessing step in our IT-CAD system.
authors.affiliation: Digital Advanced Imaging Laboratories, Department of Radiology, Duke University Medical Center, 2424 Erwin Road, Hock Plaza, Suite 302, Durham, NC 27710, USA. georgia.tourassi@duke.edu
authors.forename: Georgia D
authors.initials: GD
authors.lastname: Tourassi
authors.forename: Robert
authors.initials: R
authors.lastname: Ike
authors.forename: Swatee
authors.initials: S
authors.lastname: Singh
authors.forename: Brian
authors.initials: B
authors.lastname: Harrawood
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D007257:Information Theory; D008327:Mammography; D010363:Pattern Recognition, Automated / Q000379:methods; D012372:ROC Curve; D011856:Radiographic Image Enhancement; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique