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dc.contributor.author M. en_US
dc.contributor.author A. en_US
dc.contributor.author Reyer en_US
dc.contributor.author R. en_US
dc.contributor.author L. en_US
dc.date.accessioned 2008-03-04T16:14:36Z
dc.date.available 2008-03-04T16:14:36Z
dc.date.issued 2002 en_US
dc.identifier http://dx.doi.org/10.1155/S1110865702203029 en_US
dc.identifier.citation Holubinka , M , Davis , A , Zwiggelaar , R , Marti , R & Blot , L 2002 , ' Automated quality assurance applied to mammographic imaging ' EURASIP Journal of Applied Signal Processing , vol 7 , pp. 736-745 . , 10.1155/S1110865702203029 en_US
dc.identifier.other PURE: 75958 en_US
dc.identifier.other dspace: 2160/527 en_US
dc.identifier.uri http://hdl.handle.net/2160/527
dc.description.abstract 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. en_US
dc.format.extent 10 en_US
dc.relation.ispartof EURASIP Journal of Applied Signal Processing en_US
dc.title Automated quality assurance applied to mammographic imaging en_US
dc.contributor.pbl Department of Computer Science en_US
dc.contributor.pbl Vision, Graphics and Visualisation Group en_US


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