title: A comparison of breast tissue classification techniques.
abstract: It is widely accepted in the medical community that breast tissue density is an important risk factor for the development of breast cancer. Thus, the development of reliable automatic methods for classification of breast tissue is justified and necessary. Although different approaches in this area have been proposed in recent years, only a few are based on the BIRADS classification standard. In this paper we review different strategies for extracting features in tissue classification systems, and demonstrate, not only the feasibility of estimating breast density using automatic computer vision techniques, but also the benefits of segmentation of the breast based on internal tissue information. The evaluation of the methods is based on the full MIAS database classified according to BIRADS categories, and agreement between automatic and manual classification of 82% was obtained.
authors.affiliation: Institute of Informatics and Applications, University of Girona Campus Montilivi, Ed. P-IV, 17071, Girona, Spain. aoliver@eia.udg.es
authors.forename: Arnau
authors.initials: A
authors.lastname: Oliver
authors.forename: Jordi
authors.initials: J
authors.lastname: Freixenet
authors.forename: Robert
authors.initials: R
authors.lastname: Martí
authors.forename: Reyer
authors.initials: R
authors.lastname: Zwiggelaar
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000145:classification* / Q000000981:diagnostic imaging*; D006801:Humans; D008327:Mammography / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D011856:Radiographic Image Enhancement / Q000379:methods; D011857:Radiographic Image Interpretation, Computer-Assisted / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity