Linked Life Data

17354741

Source:http://linkedlifedata.com/resource/pubmed/id/17354741

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2007-3-14
pubmed:abstractText
The presence of speckle in ultrasound images causes many spurious local minima in the energy function of active contours. These minima trap the segmentation prematurely under gradient descent and cause the algorithm to fail. This paper presents a substantially new reformulation of Tunneling Descent, which is a deterministic technique to escape from unwanted local minima. In the new formulation, the evolving curve is represented by level sets, and the evolution strategy is obtained as a sequence of constrained minimizations. The algorithm is used to segment the endocardium in 115 short axis cardiac ultrasound images. All segmentations are achieved without tweaking the energy function or numerical parameters. Experimental evaluation of the results shows that the algorithm overcomes multiple local minima to give segmentations that are considerably more accurate than conventional techniques.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1011-2499
pubmed:author
pubmed:issnType
Print
pubmed:volume
19
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
750-61
pubmed:dateRevised
2007-12-3
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Tunneling descent level set segmentation of ultrasound images.
pubmed:affiliation
Dept. of Electrical Engineering, Yale University, New Haven, CT 06520, USA.
pubmed:publicationType
Journal Article, Evaluation Studies, Research Support, N.I.H., Extramural