Non-parametric Bayesian models of response function in dynamic image sequences

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F16%3A00456983" target="_blank" >RIV/67985556:_____/16:00456983 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.cviu.2015.11.010" target="_blank" >http://dx.doi.org/10.1016/j.cviu.2015.11.010</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cviu.2015.11.010" target="_blank" >10.1016/j.cviu.2015.11.010</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Non-parametric Bayesian models of response function in dynamic image sequences

Popis výsledku v původním jazyce

Estimation of response functions is an important task in dynamic medical imaging. This task arises for example in dynamic renal scintigraphy, where impulse response or retention functions are estimated, or in functional magnetic resonance imaging where hemodynamic response functions are required. These functions can not be observed directly and their estimation is complicated because the recorded images are subject to superposition of underlying signals. Therefore, the response functions are estimated via blind source separation and deconvolution. Performance of this algorithm heavily depends on the used models of the response functions. Response functions in real image sequences are rather complicated and finding a suitable parametric form is problematic. In this paper, we study estimation of the response functions using non-parametric Bayesian priors. These priors were designed to favor desirable properties of the functions, such as sparsity or smoothness.

Název v anglickém jazyce

Non-parametric Bayesian models of response function in dynamic image sequences

Popis výsledku anglicky

Estimation of response functions is an important task in dynamic medical imaging. This task arises for example in dynamic renal scintigraphy, where impulse response or retention functions are estimated, or in functional magnetic resonance imaging where hemodynamic response functions are required. These functions can not be observed directly and their estimation is complicated because the recorded images are subject to superposition of underlying signals. Therefore, the response functions are estimated via blind source separation and deconvolution. Performance of this algorithm heavily depends on the used models of the response functions. Response functions in real image sequences are rather complicated and finding a suitable parametric form is problematic. In this paper, we study estimation of the response functions using non-parametric Bayesian priors. These priors were designed to favor desirable properties of the functions, such as sparsity or smoothness.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

—

Projekt

<a href="/cs/project/GA13-29225S" target="_blank" >GA13-29225S: Slepá dekonvoluce obrazu v limitních podmínkách</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Computer Vision and Image Understanding

ISSN

1077-3142

e-ISSN

—

Svazek periodika

151

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

11

Strana od-do

90-100

Kód UT WoS článku

000385338900009

EID výsledku v databázi Scopus

2-s2.0-84990911120