title: Content-based image retrieval applied to BI-RADS tissue classification in screening mammography.
abstract: OBJECTIVE
  To present a content-based image retrieval (CBIR) system that supports the classification of breast tissue density and can be used in the processing chain to adapt parameters for lesion segmentation and classification.
  
  
  METHODS
  Breast density is characterized by image texture using singular value decomposition (SVD) and histograms. Pattern similarity is computed by a support vector machine (SVM) to separate the four BI-RADS tissue categories. The crucial number of remaining singular values is varied (SVD), and linear, radial, and polynomial kernels are investigated (SVM). The system is supported by a large reference database for training and evaluation. Experiments are based on 5-fold cross validation.
  
  
  RESULTS
  Adopted from DDSM, MIAS, LLNL, and RWTH datasets, the reference database is composed of over 10 000 various mammograms with unified and reliable ground truth. An average precision of 82.14% is obtained using 25 singular values (SVD), polynomial kernel and the one-against-one (SVM).
  
  
  CONCLUSIONS
  Breast density characterization using SVD allied with SVM for image retrieval enable the development of a CBIR system that can effectively aid radiologists in their diagnosis.
authors.affiliation: Júlia Epischina Engrácia de Oliveira, Arnaldo de Albuquerque Araújo, Department of Computer Science, Universidade Federal de Minas Gerais, 31270-901, Belo Horizonte, MG, Brazil.
authors.forename: Júlia Epischina Engrácia
authors.initials: JE
authors.lastname: de Oliveira
authors.forename: Arnaldo
authors.initials: A
authors.lastname: de Albuquerque Araújo
authors.forename: Thomas M
authors.initials: TM
authors.lastname: Deserno