A similarity study between the query mass and retrieved masses using decision tree content-based image retrieval (DTCBIR) CADx system for characterization of ultrasound breast mass images

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 nine-point similarity scale (1=very dissimilar, 9=very similar). For DTb-lda, DTL-lda, DTb-full, DTL-full and DTLs-full, the 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 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.