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

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@Article{Howard:2008:JBB,

• author = "Daniel Howard and Simon C. Roberts and Conor Ryan and Adrian Brezulianu",
• title = "Textural Classification of Mammographic Parenchymal Patterns with the SONNET Selforganizing Neural Network",
• journal = "Journal of Biomedicine and Biotechnology",
• year = "2008",
• volume = "2008",
• pages = "526343",
• month = jul # " 22",
• keywords = "genetic algorithms, genetic programming",
• DOI = "doi:10.1155/2008/526343",
• 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 self organizing 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 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.",
• notes = "PMID: {"}As the [previously evolved] data crawler has been developed for target detection in imagery, it is highly transferable to the problem of lesion detection in mammograms. The crawler could scrutinize mammogram areas which possess the greatest asymmetry and thus focus on candidate lesions. The evolutionary approach allows the crawler to discover its own multiscale features which best locate lesions.{"}",

}

Genetic Programming entries for Daniel Howard Simon C Roberts Conor Ryan Adrian Brezulianu

Citations