Automated quality assurance applied to mammographic imaging

Lilian Blot, Anne Davis, Mike Holubinka, Robert Marti, Reyer Zwiggelaar

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


Quality control in mammography is based upon subjective interpretation of the image quality of a test phantom. In order to suppress subjectivity due to the human observer, automated computer analysis of the Leeds TOR(MAM) test phantom is investigated. Texture analysis via grey-level co-occurrence matrices is used to detect structures in the test object. Scoring of the substructures in the phantom is based on grey-level differences between regions and information from grey-level co-occurrence matrices. The results from scoring groups of particles within the phantom are presented.
Original languageEnglish
Article number647019
JournalEURASIP Journal on Applied Signal Processing
Publication statusPublished - 2002

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