title: Segmentation of the fibro-glandular disc in mammograms using Gaussian mixture modelling.
abstract: The paper presents a technique for the segmentation of the fibro-glandular disc in mammograms based upon a statistical model of breast density. The density function of the model was represented by a mixture of up to four weighted Gaussians, each one corresponding to a specific density class in the breast. The parameters of the model and the number of tissue classes in the breast were determined using the expectation-maximisation algorithm and the minimum description length method. Grey-level statistics of the pectoral muscle were used to determine the tissue categories that are likely to represent the fibro-glandular disc. The method was applied to 84 medio-lateral oblique mammograms from the Mini-MIAS database. The results of the segmented fibro-glandular disc were assessed by a radiologist using the original and the segmented images, with reference to a ranking table categorising the results of segmentation as: 1: excellent; 2: good; 3: average; 4: poor; and 5: complete failure. Of the 84 cases analysed, 64.3% were rated as excellent, 16.7% were rated as good, 10.7% were rated as average, and 4.7% were rated as poor; only 3.6% of the cases were rated as a complete failure with regard to segmentation of the fibro-glandular disc.
authors.affiliation: Department of Electrical Engineering, University of São Paulo, São Carlos, Brazil.
authors.forename: R J
authors.initials: RJ
authors.lastname: Ferrari
authors.forename: R M
authors.initials: RM
authors.lastname: Rangayyan
authors.forename: R A
authors.initials: RA
authors.lastname: Borges
authors.forename: A F
authors.initials: AF
authors.lastname: Frère
mesh_terms: D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008327:Mammography / Q000379:methods*; D008954:Models, Biological; D016011:Normal Distribution