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Statements

Subject Item
n2:RIV%2F00216305%3A26220%2F14%3APU110572%21RIV15-MSM-26220___
rdf:type
n11:Vysledek skos:Concept
dcterms:description
Using magnetic resonance tomography to scan biological tissues is currently a very dynamic approach. Based on various image parameters, the method enables us to analyze tissue properties, recognize healthy and pathological tissues, and diagnose the disease or indicate its progression. These activities are then necessarily accompanied by the processing of the acquired images. The paper introduces a comparison of statistical tools for the trainable segmentation of multiparametric data obtained through magnetic resonance tomography. In this context, the author briefly compares various available tools (Weka, Slicer3D, and RapidMiner) in view of the input data training and testing, applicability of the classification models, and ability of the input/output data to be extended with other systems for further processing. The paper also describes as a multiparametric task the segmentation of a brain tumor performed with real MR data. The source of the data consists in T1 and T2-weighted images. The proposed se Using magnetic resonance tomography to scan biological tissues is currently a very dynamic approach. Based on various image parameters, the method enables us to analyze tissue properties, recognize healthy and pathological tissues, and diagnose the disease or indicate its progression. These activities are then necessarily accompanied by the processing of the acquired images. The paper introduces a comparison of statistical tools for the trainable segmentation of multiparametric data obtained through magnetic resonance tomography. In this context, the author briefly compares various available tools (Weka, Slicer3D, and RapidMiner) in view of the input data training and testing, applicability of the classification models, and ability of the input/output data to be extended with other systems for further processing. The paper also describes as a multiparametric task the segmentation of a brain tumor performed with real MR data. The source of the data consists in T1 and T2-weighted images. The proposed se
dcterms:title
Multiparametric Biological Tissue Analysis: A Survey of Image Processing Tools Multiparametric Biological Tissue Analysis: A Survey of Image Processing Tools
skos:prefLabel
Multiparametric Biological Tissue Analysis: A Survey of Image Processing Tools Multiparametric Biological Tissue Analysis: A Survey of Image Processing Tools
skos:notation
RIV/00216305:26220/14:PU110572!RIV15-MSM-26220___
n3:aktivita
n5:P
n3:aktivity
P(EE2.3.30.0039), P(GAP102/12/1104)
n3:dodaniDat
n21:2015
n3:domaciTvurceVysledku
n16:1157000
n3:druhVysledku
n14:D
n3:duvernostUdaju
n8:S
n3:entitaPredkladatele
n12:predkladatel
n3:idSjednocenehoVysledku
31069
n3:idVysledku
RIV/00216305:26220/14:PU110572
n3:jazykVysledku
n19:eng
n3:klicovaSlova
SVM, image segmentation, data mining
n3:klicoveSlovo
n4:image%20segmentation n4:SVM n4:data%20mining
n3:kontrolniKodProRIV
[DA744C2E6DA8]
n3:mistoKonaniAkce
Guangzhou
n3:mistoVydani
Guangzhou, Čína
n3:nazevZdroje
Proceedings of PIERS 2014 in Guangzhou
n3:obor
n9:JA
n3:pocetDomacichTvurcuVysledku
1
n3:pocetTvurcuVysledku
1
n3:projekt
n15:EE2.3.30.0039 n15:GAP102%2F12%2F1104
n3:rokUplatneniVysledku
n21:2014
n3:tvurceVysledku
Mikulka, Jan
n3:typAkce
n18:WRD
n3:zahajeniAkce
2014-08-25+02:00
s:numberOfPages
4
n6:hasPublisher
Neuveden
n17:isbn
978-1-934142-28-8
n10:organizacniJednotka
26220