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Journal of Biomedicine and Biotechnology
Volume 2008, Article ID 526343, 11 pages
Research Article

Textural Classification of Mammographic Parenchymal Patterns with the SONNET Selforganizing Neural Network

1QinetiQ, St Andrews Road, Malvern, Worcestershire WR14 3PS, UK
2Department of Computer Science and Information Systems, College of Informatics and Electronics, University of Limerick, Ireland
3Faculty of Electronics and Telecommunications, “Gh.Asach” Technical University of Iasi, 700050 Iasi IS, Romania

Received 12 September 2007; Accepted 16 January 2008

Academic Editor: Halima Bensmail

Copyright © 2008 Daniel Howard et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In nationwide mammography screening, thousands of mammography examinations must be processed. Each consists of two standard views of each breast, and each mammogram must be visually examined by an experienced radiologist to assess it for any anomalies. The ability to detect an anomaly in mammographic texture is important to successful outcomes in mammography screening and, in this study, a large number of mammograms were digitized with a highly accurate scanner; and textural features were derived from the mammograms as input data to a SONNET selforganizing neural network. The paper discusses how SONNET was used to produce a taxonomic organization of the mammography archive in an unsupervised manner. This process is subject to certain choices of SONNET parameters, in these numerical experiments using the craniocaudal view, and typically produced O(10), for example, 39 mammogram classes, by analysis of features from O( 1 0 3 ) mammogram images. The mammogram taxonomy captured typical subtleties to discriminate mammograms, and it is submitted that this may be exploited to aid the detection of mammographic anomalies, for example, by acting as a preprocessing stage to simplify the task for a computational detection scheme, or by ordering mammography examinations by mammogram taxonomic class prior to screening in order to encourage more successful visual examination during screening. The resulting taxonomy may help train screening radiologists and conceivably help to settle legal cases concerning a mammography screening examination because the taxonomy can reveal the frequency of mammographic patterns in a population.