bibtype C - Conference Paper (international conference)
ARLID 0361017
utime 20240103195334.7
mtime 20110719235959.9
title (primary) (eng) Factor Analysis Of Scintigraphic Image Sequences With Integrated Convolution Model Of Factor Curves
specification
page_count 7 s.
media_type www
serial
ARLID cav_un_epca*0361016
ISBN 978-0-88986-889-2
title Proceedings of the second international conference on computational bioscience
page_num 1-7
publisher
place Cambridge, UK
name IASTED
year 2011
keyword factor analysis
keyword blind decomvolution
keyword image sequences
author (primary)
ARLID cav_un_auth*0101207
name1 Šmídl
name2 Václav
full_dept (cz) Adaptivní systémy
full_dept (eng) Department of Adaptive Systems
department (cz) AS
department (eng) AS
institution UTIA-B
full_dept Department of Adaptive Systems
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0267768
name1 Tichý
name2 Ondřej
full_dept (cz) Adaptivní systémy
full_dept Department of Adaptive Systems
department (cz) AS
department AS
institution UTIA-B
full_dept Department of Adaptive Systems
fullinstit Ústav teorie informace a automatizace AV ČR, v. v. i.
author
ARLID cav_un_auth*0211467
name1 Šámal
name2 M.
country CZ
source
url http://library.utia.cas.cz/separaty/2011/AS/smidl-factor analysis of scintigraphic image sequences with integrated convolution model of factor curves.pdf
cas_special
project
project_id 303/07/0950
agency GA ČR
country CZ
ARLID cav_un_auth*0273073
research CEZ:AV0Z10750506
abstract (eng) 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.
action
ARLID cav_un_auth*0273072
name The Second IASTED International Conference on Computational Bioscience
place Cambridge
dates 11.07.2011-13.07.2011
country GB
reportyear 2012
RIV BB
num_of_auth 3
permalink http://hdl.handle.net/11104/0198434
arlyear 2011
mrcbU63 cav_un_epca*0361016 Proceedings of the second international conference on computational bioscience 978-0-88986-889-2 1 7 Cambridge, UK IASTED 2011