title: Use of the cross-correlation component of the multiscale structural similarity metric (R* metric) for the evaluation of medical images.
abstract: OBJECTIVE
  The aim of the present work is to analyze the potential of the cross-correlation component of the multiscale structural similarity metric (R*) to predict human performance in detail detection tasks closely related with diagnostic x-ray images. To check the effectiveness of R*, the authors have initially applied this metric to a contrast detail detection task.
  
  
  METHODS
  Threshold contrast visibility using the R* metric was determined for two sets of images of a contrast-detail phantom (CDMAM). Results from R* and human observers were compared as far as the contrast threshold was concerned. A comparison between the R* metric and two algorithms currently used to evaluate CDMAM images was also performed.
  
  
  RESULTS
  Similar trends for the CDMAM detection task of human observers and R* were found in this study. Threshold contrast visibility values using R* are statistically indistinguishable from those obtained by human observers (F-test statistics: p > 0.05).
  
  
  CONCLUSIONS
  These results using R* show that it could be used to mimic human observers for certain tasks, such as the determination of contrast detail curves in the presence of uniform random noise backgrounds. The R* metric could also outperform other metrics and algorithms currently used to evaluate CDMAM images and can automate this evaluation task.
authors.affiliation: Department of Radiology, Faculty of Medicine, Complutense University, 28040 Madrid, Spain. gprietor@med.ucm.es
authors.forename: Gabriel
authors.initials: G
authors.lastname: Prieto
authors.forename: Eduardo
authors.initials: E
authors.lastname: Guibelalde
authors.forename: Margarita
authors.initials: M
authors.lastname: Chevalier
authors.forename: Agustín
authors.initials: A
authors.lastname: Turrero
mesh_terms: D000465:Algorithms; D005260:Female; D006801:Humans; D008327:Mammography / Q000706:statistics & numerical data; D015588:Observer Variation; D019047:Phantoms, Imaging; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D012044:Regression Analysis