Joint Segmentation and Registration Through the Duality of Congealing and Maximum Likelihood Estimate

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F15%3A00235469" target="_blank" >RIV/68407700:21230/15:00235469 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1007/978-3-319-19992-4_27" target="_blank" >http://dx.doi.org/10.1007/978-3-319-19992-4_27</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-319-19992-4_27" target="_blank" >10.1007/978-3-319-19992-4_27</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Joint Segmentation and Registration Through the Duality of Congealing and Maximum Likelihood Estimate

Popis výsledku v původním jazyce

In this paper we consider the task of joint registration and segmentation. A popular method which aligns images and simultaneously estimates a simple statistical shape model was proposed by E. Learned-Miller and is known as congealing. It considers the entropy of a simple, pixel-wise independent distribution as the objective function for searching the unknown transformations. Besides being intuitive and appealing, this idea raises several theoretical and practical questions, which we try to answer in this paper. First, we analyse the approach theoretically and show that the original congealing is in fact the DC-dual task (difference of convex functions) for a properly formulated Maximum Likelihood estimation task. This interpretation immediately leadsto a different choice for the algorithm which is substantially simpler than the known congealing algorithm. The second contribution is to show, how to generalise the task for models in which the shape prior is formulated in terms of segme

Název v anglickém jazyce

Joint Segmentation and Registration Through the Duality of Congealing and Maximum Likelihood Estimate

Popis výsledku anglicky

In this paper we consider the task of joint registration and segmentation. A popular method which aligns images and simultaneously estimates a simple statistical shape model was proposed by E. Learned-Miller and is known as congealing. It considers the entropy of a simple, pixel-wise independent distribution as the objective function for searching the unknown transformations. Besides being intuitive and appealing, this idea raises several theoretical and practical questions, which we try to answer in this paper. First, we analyse the approach theoretically and show that the original congealing is in fact the DC-dual task (difference of convex functions) for a properly formulated Maximum Likelihood estimation task. This interpretation immediately leadsto a different choice for the algorithm which is substantially simpler than the known congealing algorithm. The second contribution is to show, how to generalise the task for models in which the shape prior is formulated in terms of segme

Druh

D - Stať ve sborníku

CEP obor

JD - Využití počítačů, robotika a její aplikace

OECD FORD obor

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Projekt

<a href="/cs/project/GA14-21421S" target="_blank" >GA14-21421S: Automatická analýza prostorových vzorů genové exprese</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

Information Processing in Medical Imaging

ISBN

978-3-319-19991-7

ISSN

0302-9743

e-ISSN

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Počet stran výsledku

12

Strana od-do

351-362

Název nakladatele

Springer

Místo vydání

Heidelberg

Místo konání akce

Isle of Skye

Datum konání akce

28. 6. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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