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Automated Quality Assurance Applied to Mammographic Imaging

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.

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Correspondence to Lilian Blot.

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Blot, L., Davis, A., Holubinka, M. et al. Automated Quality Assurance Applied to Mammographic Imaging. EURASIP J. Adv. Signal Process. 2002, 647019 (2002). https://doi.org/10.1155/S1110865702203029

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Keywords

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