Automated quality assurance applied to mammographic imaging

Lilian Blot; Anne Davis; Mike Holubinka; Robert Marti; Reyer Zwiggelaar

doi:10.1155/S1110865702203029

Automated quality assurance applied to mammographic imaging

Lilian Blot
, Anne Davis
, Mike Holubinka
, Robert Marti
, Reyer Zwiggelaar

School of Computing Sciences

Research output: Contribution to journal › Article › peer-review

4 Citations (Scopus)

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.

Original language	English
Article number	647019
Journal	EURASIP Journal on Applied Signal Processing
Volume	2002
DOIs	https://doi.org/10.1155/S1110865702203029
Publication status	Published - 2002

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10.1155/S1110865702203029

Cite this

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title = "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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AU - Davis, Anne

AU - Holubinka, Mike

AU - Marti, Robert

AU - Zwiggelaar, Reyer

PY - 2002

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N2 - 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.

AB - 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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DO - 10.1155/S1110865702203029

M3 - Article

SN - 1110-8657

VL - 2002

JO - EURASIP Journal on Applied Signal Processing

JF - EURASIP Journal on Applied Signal Processing

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