Open Access

Automated Quality Assurance Applied to Mammographic Imaging

  • Lilian Blot1Email author,
  • Anne Davis2,
  • Mike Holubinka2,
  • Robert Martí1 and
  • Reyer Zwiggelaar1
EURASIP Journal on Advances in Signal Processing20022002:647019

DOI: 10.1155/S1110865702203029

Received: 31 July 2001

Published: 24 July 2002

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
(2)
Portsmouth Hospitals, NHS Trust

Copyright

© Blot et al. 2002