We are developing a Decision Tree Content-Based Image Retrieval (DTCBIR) CADx scheme to assist
radiologists in characterization of breast masses on ultrasound (US) images. 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, the 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 the feature vector of the query and those of the selected references. For each DTCBIR
configuration, we investigated the use of the full feature set and the subset of features selected by the stepwise linear
discriminant analysis (LDA) and simplex optimization method, resulting in six retrieval methods. Among the six
methods, we selected five, DTb-lda, DTL-lda, DTb-full, DTL-full and DTLs-full, for the observer study. For a query
mass, three most similar masses were retrieved with each method and were presented to the radiologists in random order.
Three MQSA radiologists rated the similarity between the query mass and the computer-retrieved masses using a ninepoint
similarity scale (1=very dissimilar, 9=very similar). For DTb-lda, DTL-lda, DTb-full, DTL-full and DTLs-full, the
average Az 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 the average
similarity ratings were 5.00, 5.41, 4.96, 5.33 and 5.13, respectively. Although the DTb measures had the best
classification performance among the DTCBIRs studied, and DTLs had the worst performance, DTLs-full obtained
higher similarity ratings than the DTb measures.