Textural Classification of Mammographic Parenchymal Patterns with the SONNET Selforganizing Neural Network
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @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