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.
This article is part of the series Applied Visual Inspection.
Automated Quality Assurance Applied to Mammographic Imaging
1 School of Information Systems, University of East Anglia, Norwich NR4-7TJ, UK
2 Portsmouth Hospitals NHS Trust PO3 6AD, Portsmouth, UK
EURASIP Journal on Advances in Signal Processing 2002, 2002:647019 doi:10.1155/S1110865702203029
The electronic version of this article is the complete one and can be found online at: http://asp.eurasipjournals.com/content/2002/7/647019
|Received:||31 July 2001|
|Revisions received:||13 March 2002|
|Published:||24 July 2002|
© 2002 Blot et al.