Linked Life Data

16685834

Source:http://linkedlifedata.com/resource/pubmed/id/16685834

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
Pt 1
pubmed:dateCreated
2006-5-11
pubmed:abstractText
Effective validation techniques are an essential pre-requisite for segmentation and non-rigid registration techniques to enter clinical use. These algorithms can be evaluated by calculating the overlap of corresponding test and gold-standard regions. Common overlap measures compare pairs of binary labels but it is now common for multiple labels to exist and for fractional (partial volume) labels to be used to describe multiple tissue types contributing to a single voxel. Evaluation studies may involve multiple image pairs. In this paper we use results from fuzzy set theory and fuzzy morphology to extend the definitions of existing overlap measures to accommodate multiple fractional labels. Simple formulas are provided which define single figures of merit to quantify the total overlap for ensembles of pairwise or groupwise label comparisons. A quantitative link between overlap and registration error is established by defining the overlap tolerance. Experiments are performed on publicly available labeled brain data to demonstrate the new measures in a comparison of pairwise and groupwise registration.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:author
pubmed:volume
8
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
99-106
pubmed:dateRevised
2009-12-11
pubmed:meshHeading
pubmed:year
2005
pubmed:articleTitle
Generalised overlap measures for assessment of pairwise and groupwise image registration and segmentation.
pubmed:affiliation
Centre for Medical Image Computing, Dept. of Medical Physics, University College London, UK. b.crum@ucl.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Evaluation Studies