Linked Life Data

16945569

Source:http://linkedlifedata.com/resource/pubmed/id/16945569

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6
pubmed:dateCreated
2006-12-1
pubmed:abstractText
The early detection of breast cancer greatly improves prognosis. One of the earliest signs of cancer is the formation of clusters of microcalcifications. We introduce a novel method for microcalcification detection based on a biologically inspired adaptive model of contrast detection. This model is used in conjunction with image filtering based on anisotropic diffusion and curvilinear structure removal using local energy and phase congruency. An important practical issue in automatic detection methods is the selection of parameters: we show that the parameter values for our algorithm can be estimated automatically from the image. This way, the method is made robust and essentially free of parameter tuning. We report results on mammograms from two databases and show that the detection performance can be improved by first including a normalisation scheme.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Dec
pubmed:issn
1361-8415
pubmed:author
pubmed:issnType
Print
pubmed:volume
10
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
850-62
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
A biologically inspired algorithm for microcalcification cluster detection.
pubmed:affiliation
University of Oxford, Medical Vision Laboratory, Oxford, UK. mglin@deas.harvard.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't