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Statements

Subject Item
n2:RIV%2F67985556%3A_____%2F11%3A00361017%21RIV12-AV0-67985556
rdf:type
skos:Concept n14:Vysledek
dcterms:description
Factor analysis and deconvolution are commonly used tools in analysis of time activity analysis of biological organs in scintigraphic data. Typically, these are used independently such that the output of the former is taken as an input to the latter. Each method is thus unaware of the restrictions imposed by the other and fails to respect them. In this paper, we propose a probabilistic model that integrates convolution into the factor analysis model. We develop an approximate Bayesian estimation of the model parameters based on Variational Bayes approximation. The new variant of the factor analysis model is suitable for modeling of a range of biological processes where convolution kernels are known to have restricted shapes. Properties of the new model are illustrated on analysis of data from dynamic renal scintigraphy. The proposed model provides more realistic estimates of the convolution kernels. Factor analysis and deconvolution are commonly used tools in analysis of time activity analysis of biological organs in scintigraphic data. Typically, these are used independently such that the output of the former is taken as an input to the latter. Each method is thus unaware of the restrictions imposed by the other and fails to respect them. In this paper, we propose a probabilistic model that integrates convolution into the factor analysis model. We develop an approximate Bayesian estimation of the model parameters based on Variational Bayes approximation. The new variant of the factor analysis model is suitable for modeling of a range of biological processes where convolution kernels are known to have restricted shapes. Properties of the new model are illustrated on analysis of data from dynamic renal scintigraphy. The proposed model provides more realistic estimates of the convolution kernels.
dcterms:title
Factor Analysis Of Scintigraphic Image Sequences With Integrated Convolution Model Of Factor Curves Factor Analysis Of Scintigraphic Image Sequences With Integrated Convolution Model Of Factor Curves
skos:prefLabel
Factor Analysis Of Scintigraphic Image Sequences With Integrated Convolution Model Of Factor Curves Factor Analysis Of Scintigraphic Image Sequences With Integrated Convolution Model Of Factor Curves
skos:notation
RIV/67985556:_____/11:00361017!RIV12-AV0-67985556
n14:predkladatel
n15:ico%3A67985556
n4:aktivita
n7:Z
n4:aktivity
Z(AV0Z10750506)
n4:dodaniDat
n6:2012
n4:domaciTvurceVysledku
n5:6618812 n5:6082394
n4:druhVysledku
n16:D
n4:duvernostUdaju
n21:S
n4:entitaPredkladatele
n10:predkladatel
n4:idSjednocenehoVysledku
199183
n4:idVysledku
RIV/67985556:_____/11:00361017
n4:jazykVysledku
n13:eng
n4:klicovaSlova
factor analysis; blind decomvolution; image sequences
n4:klicoveSlovo
n8:blind%20decomvolution n8:factor%20analysis n8:image%20sequences
n4:kontrolniKodProRIV
[1932CEC3A168]
n4:mistoKonaniAkce
Cambridge
n4:mistoVydani
Cambridge, UK
n4:nazevZdroje
Proceedings of the second international conference on computational bioscience
n4:obor
n11:BB
n4:pocetDomacichTvurcuVysledku
2
n4:pocetTvurcuVysledku
3
n4:rokUplatneniVysledku
n6:2011
n4:tvurceVysledku
Šámal, M. Šmídl, Václav Tichý, Ondřej
n4:typAkce
n18:WRD
n4:zahajeniAkce
2011-07-11+02:00
n4:zamer
n17:AV0Z10750506
s:numberOfPages
7
n9:hasPublisher
IASTED
n12:isbn
978-0-88986-889-2