Linked Life Data

19506953

Source:http://linkedlifedata.com/resource/pubmed/id/19506953

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
5
pubmed:dateCreated
2010-10-6
pubmed:abstractText
Studies reported in the literature indicate that the increase in the breast density is one of the strongest indicators of developing breast cancer. In this paper, we present an approach to automatically evaluate the density of a breast by segmenting its internal parenchyma in either fatty or dense class. Our approach is based on a statistical analysis of each pixel neighbourhood for modelling both tissue types. Therefore, we provide connected density clusters taking the spatial information of the breast into account. With the aim of showing the robustness of our approach, the experiments are performed using two different databases: the well-known Mammographic Image Analysis Society digitised database and a new full-field digital database of mammograms from which we have annotations provided by radiologists. Quantitative and qualitative results show that our approach is able to correctly detect dense breasts, segmenting the tissue type accordingly.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Oct
pubmed:issn
1618-727X
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
23
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
527-37
pubmed:dateRevised
2011-10-3
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
A statistical approach for breast density segmentation.
pubmed:affiliation
Department of Computer Architecture and Technology, IIiA-IdIBGi, University of Girona, Campus Montilivi, Ed. P-IV, 17071 Girona, Spain. aoliver@eia.udg.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't