Fuzzy c-means with wavelet filtration for MR image segmentation

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86092828" target="_blank" >RIV/61989100:27240/14:86092828 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27740/14:86092828

Výsledek na webu

<a href="http://dx.doi.org/10.1109/NaBIC.2014.6921884" target="_blank" >http://dx.doi.org/10.1109/NaBIC.2014.6921884</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/NaBIC.2014.6921884" target="_blank" >10.1109/NaBIC.2014.6921884</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fuzzy c-means with wavelet filtration for MR image segmentation

Popis výsledku v původním jazyce

In this paper, we present an image segmentation technique based on fuzzy c-means (FCM) incorporated with wavelet domain noise filtration. With the use of image noise feature estimation composed of preliminary coefficient classification and wavelet domainindicator, a filter for balancing the preservation of relevant details against the degree of noise reduction can be created. The filter is further incorporated with FCM algorithm into the membership function for clustering. This approach allows FCM notonly to exploit useful spatial information, but also dynamically minimize clustering errors caused by common noise in medical images. Experimental results suggest its usefulness for reducing FCM clustering noise sensitivity. In MR image segmentation applications, the proposed method outperforms other FCM variations, in terms of quantitative performance measure and visual quality. 2014 IEEE.

Název v anglickém jazyce

Fuzzy c-means with wavelet filtration for MR image segmentation

Popis výsledku anglicky

In this paper, we present an image segmentation technique based on fuzzy c-means (FCM) incorporated with wavelet domain noise filtration. With the use of image noise feature estimation composed of preliminary coefficient classification and wavelet domainindicator, a filter for balancing the preservation of relevant details against the degree of noise reduction can be created. The filter is further incorporated with FCM algorithm into the membership function for clustering. This approach allows FCM notonly to exploit useful spatial information, but also dynamically minimize clustering errors caused by common noise in medical images. Experimental results suggest its usefulness for reducing FCM clustering noise sensitivity. In MR image segmentation applications, the proposed method outperforms other FCM variations, in terms of quantitative performance measure and visual quality. 2014 IEEE.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2014

Kód důvěrnosti údajů

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

Název statě ve sborníku

NaBIC 2014 ; CASoN 2014 : July 30-31, Porto, Portugal

ISBN

978-1-4799-5937-2

ISSN

—

e-ISSN

—

Počet stran výsledku

5

Strana od-do

12-16

Název nakladatele

IEEE

Místo vydání

New York

Místo konání akce

Porto

Datum konání akce

30. 7. 2014

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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