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

Result code in 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>

Result on the web

<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>

Result language

angličtina

Original language name

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

Original language description

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.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

BB - Applied statistics, operational research

OECD FORD branch

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Project

<a href="/en/project/GA13-29225S" target="_blank" >GA13-29225S: Image Blind Deconvolution in Demanding Conditions</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2016

Confidentiality

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

Name of the periodical

Computer Vision and Image Understanding

ISSN

1077-3142

e-ISSN

—

Volume of the periodical

151

Issue of the periodical within the volume

1

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

11

Pages from-to

90-100

UT code for WoS article

000385338900009

EID of the result in the Scopus database

2-s2.0-84990911120