title: Detecting film-screen artifacts in mammography using a model-based approach.
abstract: Microcalcifications can be one of the earliest signs of breast cancer. Unfortunately, their appearance in mammograms can be mimicked by dust and dirt entering the imaging process and this has been shown previously to lead to false positives. We use a model of the imaging process and, in particular, the blurring functions inherent within it to detect the film-screen artifacts caused by dust and dirt and, thus, reduce false-positives. A crucial facet of the work is the choice of the correct image representation upon which to perform the image processing. After extensive testing, our algorithm has identified no microcalcifications as being artifacts and has an artifact detection rate of approaching 96%.
authors.affiliation: Medical Vision Laboratory, Engineering Science, Oxford University, U.K. rph@oxiva.com
authors.forename: R
authors.initials: R
authors.lastname: Highnam
authors.forename: M
authors.initials: M
authors.lastname: Brady
authors.forename: R
authors.initials: R
authors.lastname: English
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001943:Breast Neoplasms / Q000000981:diagnostic imaging; D002114:Calcinosis / Q000000981:diagnostic imaging; D004391:Dust; D005189:False Positive Reactions; D005260:Female; D006801:Humans; D008327:Mammography / Q000379:methods* / Q000706:statistics & numerical data; D008954:Models, Biological; D019047:Phantoms, Imaging / Q000706:statistics & numerical data; D014963:X-Ray Intensifying Screens / Q000706:statistics & numerical data
chemical_list: D004391:Dust