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Proceedings Paper

Classifying mammograms by density: rationale and preliminary results
Author(s): Saki Hajnal; Paul Taylor; Marie-Helene Dilhuydy; Beatrice Barreau; John Fox
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Paper Abstract

We are doing research on computerized techniques for classifying mammograms as dense or fatty. The hypothesis is that areas of dense tissues are the major factor making certain mammograms harder for both radiologists and computers to interpret. Automatic identification of dense mammograms might therefore permit better use of the time and skills of expert radiologists. Concentrating on the fatty mammograms could also improve the scope for computer-aided detection of abnormalities. Mammograms were independently classified by two radiologists, with a high level of inter-observer agreement. A number of local statistical and texture measures were compared, initially on manually-placed patches of the digitized images. Two strategies for automating the procedure were then compared. The most successful measure (based on grey-level skewness in small tiles) and strategy (automatic patch placement) yield an almost automatic procedure which produces a promising separation between the classes. Evaluation of a fully-automated procedure is in progress.

Paper Details

Date Published: 29 July 1993
PDF: 12 pages
Proc. SPIE 1905, Biomedical Image Processing and Biomedical Visualization, (29 July 1993); doi: 10.1117/12.148662
Show Author Affiliations
Saki Hajnal, Imperial Cancer Research Fund (United Kingdom)
Paul Taylor, Imperial Cancer Research Fund (United Kingdom)
Marie-Helene Dilhuydy, Fondation Bergonie (France)
Beatrice Barreau, Fondation Bergonie (France)
John Fox, Imperial Cancer Research Fund (United Kingdom)

Published in SPIE Proceedings Vol. 1905:
Biomedical Image Processing and Biomedical Visualization
Raj S. Acharya; Dmitry B. Goldgof, Editor(s)

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