Linked Life Data

19447703

Source:http://linkedlifedata.com/resource/pubmed/id/19447703

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
11
pubmed:dateCreated
2009-11-3
pubmed:abstractText
Uncertainties in image registration may be a significant source of errors in anatomy mapping as well as dose accumulation in radiotherapy. It is, therefore, essential to validate the accuracy of image registration. Here, we propose a method to detect areas where mono modal B-spline registration performs well and to distinguish those from areas of the same image, where the registration is likely to be less accurate. It is a stochastic approach to automatically estimate the uncertainty of the resulting displacement vector field. The coefficients resulting from the B-spline registration are subject to moderate and randomly performed variations. A quantity is proposed to characterize the local sensitivity of the similarity measure to these variations. We demonstrate the statistical dependence between the local image registration error and this quantity by calculating their mutual information. We show the significance of the statistical dependence with an approach based on random redistributions. The proposed method has the potential to divide an image into subregions which differ in the magnitude of their average registration error.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Nov
pubmed:issn
1558-0062
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
28
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1708-16
pubmed:dateRevised
2011-9-22
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
A stochastic approach to estimate the uncertainty involved in B-spline image registration.
pubmed:affiliation
Department of Medical Physics in Radiation Oncology, German Cancer Research Center, 69120 Heidelberg, Germany. m.hub@dkfz.de
pubmed:publicationType
Journal Article