Linked Life Data

17633686

Source:http://linkedlifedata.com/resource/pubmed/id/17633686

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2007-7-18
pubmed:abstractText
We describe a new approach for estimating the posterior probability of tissue labels. Conventional likelihood models are combined with a curve length prior on boundaries, and an approximate posterior distribution on labels is sought via the Mean Field approach. Optimizing the resulting estimator by gradient descent leads to a level set style algorithm where the level set functions are the logarithm-of-odds encoding of the posterior label probabilities in an unconstrained linear vector space. Applications with more than two labels are easily accommodated. The label assignment is accomplished by the Maximum A Posteriori rule, so there are no problems of "overlap" or "vacuum". We test the method on synthetic images with additive noise. In addition, we segment a magnetic resonance scan into the major brain compartments and subcortical structures.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1011-2499
pubmed:author
pubmed:issnType
Print
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
26-37
pubmed:dateRevised
2011-9-22
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Active mean fields: solving the mean field approximation in the level set framework.
pubmed:affiliation
Surgical Planning Laboratory, Harvard Medical School and Brigham and Women's Hospital, Boston, MA, USA. pohl@bwh.harvard.edu
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, Non-P.H.S., Research Support, Non-U.S. Gov't, Evaluation Studies, Research Support, N.I.H., Extramural