Linked Life Data

21096595

Source:http://linkedlifedata.com/resource/pubmed/id/21096595

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2010-11-24
pubmed:abstractText
Automatic pectoral muscle removal on medio-lateral oblique view of mammogram is an essential step for many mammographic processing algorithms. However, the wide variability in the position of the muscle contour, together with the similarity between in muscle and breast tissues makes the detection a difficult task. In this paper, we propose a two step procedure to detect the muscle contour. In a first step, the endpoints of the contour are predicted with a pair of support vector regression models; one model is trained to predict the intersection point of the contour with the top row while the other is designed for the prediction of the endpoint of the contour on the left column. Next, the muscle contour is computed as the shortest path between the two endpoints. A comprehensive comparison with manually-drawn contours reveals the strength of the proposed method.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1557-170X
pubmed:author
pubmed:issnType
Print
pubmed:volume
2010
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
3158-61
pubmed:meshHeading
pubmed:year
2010
pubmed:articleTitle
Pectoral muscle detection in mammograms based on the shortest path with endpoints learnt by SVMs.
pubmed:affiliation
INESC Porto, Faculdade de Engenharia, Universidade do Porto, Portugal. inesdomingues@gmail.com
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Evaluation Studies