title: Computer-assisted diagnosis: the classification of mammographic breast parenchymal patterns.
abstract: We have developed a method for the quantification of breast texture by using different algorithms to classify mammograms into the four patterns described by Wolfe (N1, P1, P2 and Dy). The computerized scheme employs craniocaudal views of conventional screen-film mammograms, which are digitized by a laser scanner. We used discriminant analysis to select among different feature-extraction techniques, including Fourier transform, local-contrast analysis, and grey-level distribution and quantification. The method has been evaluated on 117 clinical mammograms previously classified by five radiologists as to mammographic breast parenchymal patterns (MBPPS). The results show differences in agreement among radiologists and computer classification, depending on the Wolfe pattern: excellent for Dy (kappa = 0.77), good for P2 (kappa = 0.52) and N1 (kappa = 0.52) and poor for P1 (kappa = 0.22). Our quantitative texture measure as calculated from digital mammograms may be valuable to radiologists in their assessment of MBPP and therefore useful in establishing an index of risk for developing breast carcinoma.
authors.affiliation: Department of Electronics and Computation, University of Santiago de Compostela, Spain.
authors.forename: P G
authors.initials: PG
authors.lastname: Tahoces
authors.forename: J
authors.initials: J
authors.lastname: Correa
authors.forename: M
authors.initials: M
authors.lastname: Souto
authors.forename: L
authors.initials: L
authors.lastname: Gómez
authors.forename: J J
authors.initials: JJ
authors.lastname: Vidal
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000517:prevention & control; D003936:Diagnosis, Computer-Assisted; D005260:Female; D005583:Fourier Analysis; D006801:Humans; D008327:Mammography; D008403:Mass Screening; D012016:Reference Values