A similarity study of content-based image retrieval system for breast cancer using decision tree. Academic Article uri icon

Overview

abstract

  • PURPOSE: 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.

publication date

  • January 1, 2013

Research

keywords

  • Breast Neoplasms
  • Decision Trees
  • Diagnosis, Computer-Assisted
  • Image Interpretation, Computer-Assisted

Identity

PubMed Central ID

  • PMC3537763

Scopus Document Identifier

  • 84872024681

Digital Object Identifier (DOI)

  • 10.1007/BF00116251

PubMed ID

  • 23298117

Additional Document Info

volume

  • 40

issue

  • 1