Model Consideration for Blind Source Separation of Medical Image Sequences

Kód výsledku v IS VaVaI

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Výsledek na webu

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DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Model Consideration for Blind Source Separation of Medical Image Sequences

Popis výsledku v původním jazyce

The problem of functional analysis of medical image sequences is studied. The obtained images are assumed to be a superposition of images of underlying biological organs. This is commonly modeled as a Factor Analysis (FA) model. However, this model aloneallows for biologically impossible solutions. Therefore, we seek additional biologically motivated assumptions that can be incorporated into the model to yield better solutions. In this paper, we review additional assumptions such as convolution of timeactivity, regions of interest selection, and noise analysis. All these assumptions can be incorporated into the FA model and their parameters estimated by the Variation Bayes estimation procedure. We compare these assumptions and discuss their influenceon the resulting decomposition from diagnostic point of view. The algorithms are tested and demonstrated on real data from renal scintigraphy; however, the methodology can be used in any other imaging modality.

Název v anglickém jazyce

Model Consideration for Blind Source Separation of Medical Image Sequences

Popis výsledku anglicky

The problem of functional analysis of medical image sequences is studied. The obtained images are assumed to be a superposition of images of underlying biological organs. This is commonly modeled as a Factor Analysis (FA) model. However, this model aloneallows for biologically impossible solutions. Therefore, we seek additional biologically motivated assumptions that can be incorporated into the model to yield better solutions. In this paper, we review additional assumptions such as convolution of timeactivity, regions of interest selection, and noise analysis. All these assumptions can be incorporated into the FA model and their parameters estimated by the Variation Bayes estimation procedure. We compare these assumptions and discuss their influenceon the resulting decomposition from diagnostic point of view. The algorithms are tested and demonstrated on real data from renal scintigraphy; however, the methodology can be used in any other imaging modality.

Druh

D - Stať ve sborníku

CEP obor

BB - Aplikovaná statistika, operační výzkum

OECD FORD obor

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Projekt

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Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název statě ve sborníku

Doktorandské dny 2012

ISBN

978-80-01-05138-2

ISSN

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e-ISSN

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Počet stran výsledku

10

Strana od-do

1-10

Název nakladatele

ČVUT v Praze

Místo vydání

Praha

Místo konání akce

Praha

Datum konání akce

16. 11. 2012

Typ akce podle státní příslušnosti

CST - Celostátní akce

Kód UT WoS článku

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