21224204

Source:http://linkedlifedata.com/resource/pubmed/id/21224204

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Predicate	Object
rdf:type	pubmed:Citation
lifeskim:mentions	umls-concept:C0005847, umls-concept:C0185115, umls-concept:C0185125
pubmed:issue	5
pubmed:dateCreated	2011-1-12
pubmed:abstractText	Three-dimensional (3D) segmentation of blood vessels plays a very important role in solving some practical problems such as diagnosis of vessels diseases. Because of the effective segmentation for 2D images, the fuzzy connectedness segmentation method is introduced to extract vascular structures from 3D blood vessel volume dataset. In the experiments, three segmentation methods including thresholding method, region growing method and fuzzy connectedness method are all used to extract the vascular structures, and their results are compared. The results indicate that fuzzy connectedness method is better than thresholding method in connectivity of segmentation results, and better than region growing method in precision of segmentation results.
pubmed:language	eng
pubmed:journal	http://linkedlifedata.com/resource/pubmed/journal/101253758
pubmed:citationSubset	IM
pubmed:status	MEDLINE
pubmed:issn	1744-5485
pubmed:author	pubmed-author:LvXinrongX, pubmed-author:ZouHuaH
pubmed:issnType	Print
pubmed:volume	6
pubmed:owner	NLM
pubmed:authorsComplete	Y
pubmed:pagination	461-71
pubmed:meshHeading	pubmed-meshheading:21224204-Algorithms, pubmed-meshheading:21224204-Blood Vessels, pubmed-meshheading:21224204-Fuzzy Logic, pubmed-meshheading:21224204-Imaging, Three-Dimensional
pubmed:year	2010
pubmed:articleTitle	Application of fuzzy connectedness in 3D blood vessel extraction.
pubmed:affiliation	College of Information and Control Engineering, China University of Petroleum (East China), Dongying, 257061, Shandong, China. lxr1182@gmail.com
pubmed:publicationType	Journal Article, Research Support, Non-U.S. Gov't