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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F13%3APU104225%21RIV14-GA0-26220___
rdf:type
skos:Concept n16:Vysledek
dcterms:description
The problem most frequently encountered in the practical processing of medical images consists in the lack of instruments enabling machine evaluation of the images. A typical example of this situation is perfusion analysis of brain tumor types. The first and very significant step lies in the segmentation of individual parts of the brain tumor; after segmentation, the rate of penetration by the applied contrast agent is observed in the parts. The common method, in which a high error rate has to be considered, is to mark these tumor portions manually. Within the second step of the segmentation procedure, the monitoring of perfusion in the segmented tissues is realized together with the correlation to model cases. The quality of brain tissue segmentation exerts significant influence on the quality of evaluation of perfusion parameters; consequently, the tumor type recognition is also influenced. This means that the design of a suitable, accurate, and reproducible method constitutes a critical point withi The problem most frequently encountered in the practical processing of medical images consists in the lack of instruments enabling machine evaluation of the images. A typical example of this situation is perfusion analysis of brain tumor types. The first and very significant step lies in the segmentation of individual parts of the brain tumor; after segmentation, the rate of penetration by the applied contrast agent is observed in the parts. The common method, in which a high error rate has to be considered, is to mark these tumor portions manually. Within the second step of the segmentation procedure, the monitoring of perfusion in the segmented tissues is realized together with the correlation to model cases. The quality of brain tissue segmentation exerts significant influence on the quality of evaluation of perfusion parameters; consequently, the tumor type recognition is also influenced. This means that the design of a suitable, accurate, and reproducible method constitutes a critical point withi
dcterms:title
Segmentation of Brain Tumor Parts in Magnetic Resonance Images Segmentation of Brain Tumor Parts in Magnetic Resonance Images
skos:prefLabel
Segmentation of Brain Tumor Parts in Magnetic Resonance Images Segmentation of Brain Tumor Parts in Magnetic Resonance Images
skos:notation
RIV/00216305:26220/13:PU104225!RIV14-GA0-26220___
n16:predkladatel
n17:orjk%3A26220
n3:aktivita
n13:S n13:P
n3:aktivity
P(GAP102/11/0318), P(GAP102/12/1104), S
n3:dodaniDat
n14:2014
n3:domaciTvurceVysledku
n8:2629291 n8:1157000 n8:8928053
n3:druhVysledku
n22:D
n3:duvernostUdaju
n15:S
n3:entitaPredkladatele
n4:predkladatel
n3:idSjednocenehoVysledku
104420
n3:idVysledku
RIV/00216305:26220/13:PU104225
n3:jazykVysledku
n10:eng
n3:klicovaSlova
perfusion analysis, brain tumor segmentation, data classification, support vector machines, multi-parametric segmentation
n3:klicoveSlovo
n6:perfusion%20analysis n6:multi-parametric%20segmentation n6:support%20vector%20machines n6:brain%20tumor%20segmentation n6:data%20classification
n3:kontrolniKodProRIV
[11836ED91C17]
n3:mistoKonaniAkce
Rome
n3:mistoVydani
Neuveden
n3:nazevZdroje
36th International Conference on Telecommunications and Signal Processing
n3:obor
n9:JA
n3:pocetDomacichTvurcuVysledku
3
n3:pocetTvurcuVysledku
4
n3:projekt
n19:GAP102%2F12%2F1104 n19:GAP102%2F11%2F0318
n3:rokUplatneniVysledku
n14:2013
n3:tvurceVysledku
Gescheidtová, Eva Říha, Kamil Mikulka, Jan Burget, Radim
n3:typAkce
n20:WRD
n3:zahajeniAkce
2013-07-02+02:00
s:numberOfPages
4
n11:hasPublisher
Neuveden
n5:isbn
978-1-4799-0403-7
n21:organizacniJednotka
26220