This HTML5 document contains 42 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
n18http://linked.opendata.cz/ontology/domain/vavai/riv/typAkce/
dctermshttp://purl.org/dc/terms/
n21http://localhost/temp/predkladatel/
n20http://purl.org/net/nknouf/ns/bibtex#
n15http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n12http://linked.opendata.cz/resource/domain/vavai/projekt/
n9http://linked.opendata.cz/resource/domain/vavai/subjekt/
n7http://linked.opendata.cz/ontology/domain/vavai/
n16https://schema.org/
shttp://schema.org/
skoshttp://www.w3.org/2004/02/skos/core#
n3http://linked.opendata.cz/ontology/domain/vavai/riv/
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n4http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n22http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n10http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n17http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n6http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F00216305%3A26220%2F13%3APU103389%21RIV14-GA0-26220___/
n14http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n13http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n11http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F13%3APU103389%21RIV14-GA0-26220___
rdf:type
n7:Vysledek skos:Concept
dcterms:description
Image processing in biomedical applications is strongly developing issue. Many methods and approaches for image preprocessing, segmentation and visualization were described. This paper deals with image segmentation, concretely brain tumor segmentation. The main problem in medical practice is to recognize the type of brain or other tumor. There are many methods for tumor classification and one of them is perfusion imaging/analysis. Perfusion images are of very low contrast and they are devaluated by noise. The main idea is to identify the level of perfusion of contrast agent transported into the pathological tissue. The level of perfusion may decide on the type of tumor. The perfusion has to be monitored in tumor region, edema around the tumor region and in the interface between brain tumor and edema. The goal described in this paper is to propose a segmentation method to recognize brain tumor, edema and necrosis in structural magnetic resonance images (T1, T2) and create a binary mask that enables mea Image processing in biomedical applications is strongly developing issue. Many methods and approaches for image preprocessing, segmentation and visualization were described. This paper deals with image segmentation, concretely brain tumor segmentation. The main problem in medical practice is to recognize the type of brain or other tumor. There are many methods for tumor classification and one of them is perfusion imaging/analysis. Perfusion images are of very low contrast and they are devaluated by noise. The main idea is to identify the level of perfusion of contrast agent transported into the pathological tissue. The level of perfusion may decide on the type of tumor. The perfusion has to be monitored in tumor region, edema around the tumor region and in the interface between brain tumor and edema. The goal described in this paper is to propose a segmentation method to recognize brain tumor, edema and necrosis in structural magnetic resonance images (T1, T2) and create a binary mask that enables mea
dcterms:title
An Improved Segmentation of Brain Tumor, Edema and Necrosis An Improved Segmentation of Brain Tumor, Edema and Necrosis
skos:prefLabel
An Improved Segmentation of Brain Tumor, Edema and Necrosis An Improved Segmentation of Brain Tumor, Edema and Necrosis
skos:notation
RIV/00216305:26220/13:PU103389!RIV14-GA0-26220___
n7:predkladatel
n9:orjk%3A26220
n3:aktivita
n10:S n10:P
n3:aktivity
P(GAP102/12/1104), S
n3:dodaniDat
n11:2014
n3:domaciTvurceVysledku
n15:1157000
n3:druhVysledku
n14:D
n3:duvernostUdaju
n22:S
n3:entitaPredkladatele
n6:predkladatel
n3:idSjednocenehoVysledku
60369
n3:idVysledku
RIV/00216305:26220/13:PU103389
n3:jazykVysledku
n17:eng
n3:klicovaSlova
image segmentation, image classification, brain tumor
n3:klicoveSlovo
n4:image%20classification n4:brain%20tumor n4:image%20segmentation
n3:kontrolniKodProRIV
[9E3C3F257653]
n3:mistoKonaniAkce
Taipei
n3:mistoVydani
Neuveden
n3:nazevZdroje
Proceedings of PIERS 2013 in Taipei
n3:obor
n13:JA
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
2
n3:projekt
n12:GAP102%2F12%2F1104
n3:rokUplatneniVysledku
n11:2013
n3:tvurceVysledku
Gescheidtová, Eva Mikulka, Jan
n3:typAkce
n18:WRD
n3:zahajeniAkce
2013-03-25+01:00
s:numberOfPages
4
n20:hasPublisher
Neuveden
n16:isbn
978-1-934142-24-0
n21:organizacniJednotka
26220