Linked Life Data

10721635

Source:http://linkedlifedata.com/resource/pubmed/id/10721635

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2000-4-11
pubmed:abstractText
Thanks to its ability to yield functionally rather than anatomically-based information, the three-dimensional (3-D) SPECT imagery technique has become a great help in the diagnostic of cerebrovascular diseases. Nevertheless, due to the imaging process, the 3-D single photon emission computed tomography (SPECT) images are very blurred and, consequently, their interpretation by the clinician is often difficult and subjective. In order to improve the resolution of these 3-D images and then to facilitate their interpretation, we propose herein to extend a recent image blind deconvolution technique (called the nonnegativity support constraint-recursive inverse filtering deconvolution method) in order to improve both the spatial and the interslice resolution of SPECT volumes. This technique requires a preliminary step in order to find the support of the object to be restored. In this paper, we propose to solve this problem with an unsupervised 3-D Markovian segmentation technique. This method has been successfully tested on numerous real and simulated brain SPECT volumes, yielding very promising restoration results.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
0018-9294
pubmed:author
pubmed:issnType
Print
pubmed:volume
47
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
274-80
pubmed:dateRevised
2009-11-11
pubmed:meshHeading
pubmed:year
2000
pubmed:articleTitle
Three-dimensional blind deconvolution of SPECT images.
pubmed:affiliation
Institut National de Recherche en Informatique et Automatique, France. mignotte@iro.umontreal.ca
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't