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Statements

Subject Item
n2:RIV%2F49777513%3A23520%2F11%3A43898227%21RIV12-MSM-23520___
rdf:type
n11:Vysledek skos:Concept
dcterms:description
This paper describes a solution of CT arteriography vessel segmentation using a combination of methods for segmentation of image data. Computer tomography (CT) is one of the most useful approaches for investigating the arterial system and its pathologies. There are three ways for segmenting the vessels from the CT image - manually, fully automatically and with a user interaction. We considered the (semi)automatic approaches, because the manual way is too timeconsuming and humandependent. We used a combination of three segmentation techniques: thresholding, edge detection and region growing. Results of this method were consulted then with a medicine expert. None of these techniques can be used separately, but a combi nation of them brought very good results, which can be used in medicine. This paper describes a solution of CT arteriography vessel segmentation using a combination of methods for segmentation of image data. Computer tomography (CT) is one of the most useful approaches for investigating the arterial system and its pathologies. There are three ways for segmenting the vessels from the CT image - manually, fully automatically and with a user interaction. We considered the (semi)automatic approaches, because the manual way is too timeconsuming and humandependent. We used a combination of three segmentation techniques: thresholding, edge detection and region growing. Results of this method were consulted then with a medicine expert. None of these techniques can be used separately, but a combi nation of them brought very good results, which can be used in medicine.
dcterms:title
Segmentation of CT arteriography based on combination of segmentation methods Segmentation of CT arteriography based on combination of segmentation methods
skos:prefLabel
Segmentation of CT arteriography based on combination of segmentation methods Segmentation of CT arteriography based on combination of segmentation methods
skos:notation
RIV/49777513:23520/11:43898227!RIV12-MSM-23520___
n11:predkladatel
n13:orjk%3A23520
n3:aktivita
n14:S
n3:aktivity
S
n3:cisloPeriodika
3
n3:dodaniDat
n7:2012
n3:domaciTvurceVysledku
n10:2064014 n10:7180659 n10:8372314
n3:druhVysledku
n19:J
n3:duvernostUdaju
n17:S
n3:entitaPredkladatele
n5:predkladatel
n3:idSjednocenehoVysledku
228586
n3:idVysledku
RIV/49777513:23520/11:43898227
n3:jazykVysledku
n15:eng
n3:klicovaSlova
image processing, segmentation, vessel tree, thresholding, region growing, edge detection
n3:klicoveSlovo
n4:edge%20detection n4:region%20growing n4:vessel%20tree n4:image%20processing n4:segmentation n4:thresholding
n3:kodStatuVydavatele
RU - Ruská federace
n3:kontrolniKodProRIV
[D6E35571EDF5]
n3:nazevZdroje
Pattern Recognition and Image Analysis
n3:obor
n18:JD
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
3
n3:rokUplatneniVysledku
n7:2011
n3:svazekPeriodika
21
n3:tvurceVysledku
Železný, Miloš Pirner, Ivan Jiřík, Miroslav
s:issn
1054-6618
s:numberOfPages
3
n6:doi
10.1134/S105466181102088X
n16:organizacniJednotka
23520