Linked Life Data

17633710

Source:http://linkedlifedata.com/resource/pubmed/id/17633710

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2007-7-18
pubmed:abstractText
In this paper an algorithm for atlas-to-image non-rigid registration based on regional entropy minimization is presented. Tissue class probabilities in the atlas are registered with the intensities in the target image. The novel aspect of the paper consists in using tissue class probability maps that include the three main regions (for the brain, white matter, gray matter and csf) and a further partitioning thereof. For example, gray matter is further subdivided into basal ganglia (each of them defining its own class) and the rest (of gray matter). This guarantees a regional entropy minimization instead of just a global one. In other words, the local labels in the atlas will be adjusted in order to obtain the best explanation for the intensity distribution in the corresponding subregion of the target image.
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
320-32
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
Atlas-to-image non-rigid registration by minimization of conditional local entropy.
pubmed:affiliation
Katholieke Universiteit Leuven, Faculties of Medicine and Engineering, Medical Imaging Center (Radiology - ESAT/PSI), University Hospital Gasthuisberg, Herestraat 49, B-3000 Leuven, Belgium. Emiliano.DAgostino@esat.kuleuven.be
pubmed:publicationType
Journal Article, Evaluation Studies