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