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  • Research Article
  • Open Access

Automated Quality Assurance Applied to Mammographic Imaging

  • 1Email author,
  • 2,
  • 2,
  • 1 and
  • 1
EURASIP Journal on Advances in Signal Processing20022002:647019

https://doi.org/10.1155/S1110865702203029

  • Received: 31 July 2001
  • Published:

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.

Keywords

  • automatic quality control
  • mammographic images
  • grey-level co-occurrence matrices
  • image segmentation

Authors’ Affiliations

(1)
School of Information Systems, University of East Anglia, Norwich, NR4-7TJ, UK
(2)
Portsmouth Hospitals, NHS Trust, PO3 6AD Portsmouth, UK

Copyright

© Blot et al. 2002

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