University of Limerick Institutional Repository

Textural classification of mammographic parenchymal patterns with the sonnet selforganizing neural network

DSpace Repository

Show simple item record

dc.contributor.author Howard, Daniel
dc.contributor.author Roberts, Simon C.
dc.contributor.author Ryan, Conor
dc.contributor.author Brezulianu, Adrian
dc.date.accessioned 2018-01-08T11:49:53Z
dc.date.available 2018-01-08T11:49:53Z
dc.date.issued 2008
dc.identifier.uri http://hdl.handle.net/10344/6410
dc.description peer-reviewed en_US
dc.description.abstract 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(103) 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 bymammogram 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. en_US
dc.language.iso eng en_US
dc.publisher Hindawi Publishing Corporation en_US
dc.relation.ispartofseries Journal of Biomedicine and Biotechnology;526343
dc.relation.uri http://dx.doi.org/10.1155/2008/526343
dc.subject textural classification en_US
dc.subject mammographic parenchymal patterns en_US
dc.subject sonnet selforganizing en_US
dc.subject neural network en_US
dc.title Textural classification of mammographic parenchymal patterns with the sonnet selforganizing neural network en_US
dc.type info:eu-repo/semantics/article en_US
dc.type.supercollection all_ul_research en_US
dc.type.supercollection ul_published_reviewed en_US
dc.identifier.doi 10.1155/2008/526343
dc.rights.accessrights info:eu-repo/semantics/openAccess en_US


Files in this item

This item appears in the following Collection(s)

Show simple item record

Search ULIR


Browse

My Account

Statistics