title: A similarity study of content-based image retrieval system for breast cancer using decision tree.
abstract: OBJECTIVE
  We are developing a decision tree content-based image retrieval (DTCBIR) CADx system to assist radiologists in characterization of breast masses on ultrasound images.
  
  
  METHODS
  Three DTCBIR configurations, including decision tree with boosting (DTb), decision tree with full leaf features (DTL), and decision tree with selected leaf features (DTLs) were compared. For DTb, features of a query mass were combined first into a merged feature score and then masses with similar scores were retrieved. For DTL and DTLs, similar masses were retrieved based on the Euclidean distance between feature vectors of the query and those of selected references. For each DTCBIR configuration, we investigated the use of full feature set and subset of features selected by the stepwise linear discriminant analysis (LDA) and simplex optimization method, resulting in six retrieval methods and selected five, DTb-lda, DTL-lda, DTb-full, DTL-full, and DTLs-full, for the observer study. Three MQSA radiologists rated similarities between the query mass and computer-retrieved three most similar masses using nine-point similarity scale (9 = very similar).
  
  
  RESULTS
  For DTb-lda, DTL-lda, DTb-full, DTL-full, and DTLs-full, average A(z) values were 0.90 ± 0.03, 0.85 ± 0.04, 0.87 ± 0.04, 0.79 ± 0.05, and 0.71 ± 0.06, respectively, and average similarity ratings were 5.00, 5.41, 4.96, 5.33, and 5.13, respectively.
  
  
  CONCLUSIONS
  The DTL-lda is a promising DTCBIR CADx configuration which had simple tree structure, good classification performance, and highest similarity rating.
authors.affiliation: Department of Radiology, The University of Michigan, Ann Arbor, MI, USA.
authors.forename: Hyun-Chong
authors.initials: HC
authors.lastname: Cho
authors.forename: Lubomir
authors.initials: L
authors.lastname: Hadjiiski
authors.forename: Berkman
authors.initials: B
authors.lastname: Sahiner
authors.forename: Heang-Ping
authors.initials: HP
authors.lastname: Chan
authors.forename: Mark
authors.initials: M
authors.lastname: Helvie
authors.forename: Chintana
authors.initials: C
authors.lastname: Paramagul
authors.forename: Alexis V
authors.initials: AV
authors.lastname: Nees
mesh_terms: D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D003663:Decision Trees; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D015588:Observer Variation; D014463:Ultrasonography