Linked Life Data

10368015

Source:http://linkedlifedata.com/resource/pubmed/id/10368015

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
1999-8-20
pubmed:abstractText
We investigated a method for a fully automatic identification and interpretation process for clustered microcalcifications in mammograms. Mammographic films of 100 patients containing microcalcifications with known histology were digitized and preprocessed using standard techniques. Microcalcifications detected by an artificial neural network (ANN) were clustered and some cluster features served as the input of another ANN trained to differentiate between typical and atypical clusters, while others were fed into an ANN trained on typical clusters to evaluate these lesions. The measured sensitivity for the detection of grouped microcalcifications was 0.98. For the task of differentiation between typical and atypical clusters an Az value of 0.87 was computed, while for the diagnosis an Az value of 0.87 with a sensitivity of 0.97 and a specificity of 0.47 was obtained. The results show that a fully automatic computer system was developed for the identification and interpretation of clustered microcalcitications in mammograms with the ability to differentiate most benign lesions from malignant ones in an automatically selected subset of cases.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
0031-9155
pubmed:author
pubmed:issnType
Print
pubmed:volume
44
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1231-43
pubmed:dateRevised
2007-11-15
pubmed:meshHeading
pubmed:year
1999
pubmed:articleTitle
An automatic method for the identification and interpretation of clustered microcalcifications in mammograms.
pubmed:affiliation
Department of Radiology, Karl-Franzens-University, Graz, Austria. ferdinand.schmidt@kfunigraz.ac.at
pubmed:publicationType
Journal Article