Bayesian Blind Separation and Deconvolution of Dynamic Image Sequences Using Sparsity Priors

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F15%3A00431090" target="_blank" >RIV/67985556:_____/15:00431090 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/TMI.2014.2352791" target="_blank" >http://dx.doi.org/10.1109/TMI.2014.2352791</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/TMI.2014.2352791" target="_blank" >10.1109/TMI.2014.2352791</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bayesian Blind Separation and Deconvolution of Dynamic Image Sequences Using Sparsity Priors

Popis výsledku v původním jazyce

A common problem of imaging three-dimensional objects into image plane is superposition of the projected structures. In dynamic imaging, projection overlaps of organs and tissues complicate extraction of signals specific to individual structures with different dynamics. The problem manifests itself also in dynamic tomography as tissue mixtures are present in voxels. Separation of signals specific to dynamic structures belongs to the category of blind source separation. It is an underdetermined problem with many possible solutions. Existing separation methods select the solution that best matches their additional assumptions on the source model. We propose a novel blind source separation method based on probabilistic model of dynamic image sequences assuming each source dynamics as convolution of an input function and a source specific kernel (modeling organ impulse response or retention function). These assumptions are formalized as a Bayesian model with hierarchical prior and solved b

Název v anglickém jazyce

Bayesian Blind Separation and Deconvolution of Dynamic Image Sequences Using Sparsity Priors

Popis výsledku anglicky

A common problem of imaging three-dimensional objects into image plane is superposition of the projected structures. In dynamic imaging, projection overlaps of organs and tissues complicate extraction of signals specific to individual structures with different dynamics. The problem manifests itself also in dynamic tomography as tissue mixtures are present in voxels. Separation of signals specific to dynamic structures belongs to the category of blind source separation. It is an underdetermined problem with many possible solutions. Existing separation methods select the solution that best matches their additional assumptions on the source model. We propose a novel blind source separation method based on probabilistic model of dynamic image sequences assuming each source dynamics as convolution of an input function and a source specific kernel (modeling organ impulse response or retention function). These assumptions are formalized as a Bayesian model with hierarchical prior and solved b

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

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název periodika

IEEE Transactions on Medical Imaging

ISSN

0278-0062

e-ISSN

—

Svazek periodika

34

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

9

Strana od-do

258-266

Kód UT WoS článku

000346975900024

EID výsledku v databázi Scopus

2-s2.0-84937560793