Linked Life Data

17300915

Source:http://linkedlifedata.com/resource/pubmed/id/17300915

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2007-4-2
pubmed:abstractText
The aim of this paper is to describe a semi-automatic method of segmentation in magnetic resonance angiography (MRA). This method, based on fuzzy set theory, uses the information (gray levels) contained in the maximum intensity projection (MIP) image to segment the 3D vascular structure from slices. Tests have been carried out on vascular phantom and on clinical MRA images. This 3D segmentation method has proved to be satisfactory for the detection of vascular structures even for very complex shapes. Finally, this MIP-based approach is semi-automatic and produces a robust segmentation thanks to the contrast-to-noise ratio and to the slice profile which are taken into account to determine the membership of a voxel to the vascular structure.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Apr
pubmed:issn
0895-6111
pubmed:author
pubmed:issnType
Print
pubmed:volume
31
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
128-40
pubmed:meshHeading
pubmed:year
2007
pubmed:articleTitle
From MIP image to MRA segmentation using fuzzy set theory.
pubmed:affiliation
Inserm, U703, ThIAIS, Lille, France. m-vermandel@chru-lille.fr
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't