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Statements

Subject Item
n2:RIV%2F61989100%3A27740%2F14%3A86092828%21RIV15-MSM-27740___
rdf:type
skos:Concept n16:Vysledek
dcterms:description
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 domain indicator, 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 not only 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. 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 domain indicator, 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 not only 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.
dcterms:title
Fuzzy c-means with wavelet filtration for MR image segmentation Fuzzy c-means with wavelet filtration for MR image segmentation
skos:prefLabel
Fuzzy c-means with wavelet filtration for MR image segmentation Fuzzy c-means with wavelet filtration for MR image segmentation
skos:notation
RIV/61989100:27740/14:86092828!RIV15-MSM-27740___
n5:aktivita
n6:S
n5:aktivity
S
n5:dodaniDat
n10:2015
n5:domaciTvurceVysledku
Abraham Padath, Ajith
n5:druhVysledku
n19:D
n5:duvernostUdaju
n11:S
n5:entitaPredkladatele
n12:predkladatel
n5:idSjednocenehoVysledku
17816
n5:idVysledku
RIV/61989100:27740/14:86092828
n5:jazykVysledku
n18:eng
n5:klicovaSlova
wavelet; segmentation; noise reduction; MR image; fuzzy c-means; clustering
n5:klicoveSlovo
n7:fuzzy%20c-means n7:clustering n7:MR%20image n7:noise%20reduction n7:segmentation n7:wavelet
n5:kontrolniKodProRIV
[E0403E575820]
n5:mistoKonaniAkce
Porto
n5:mistoVydani
New York
n5:nazevZdroje
NaBIC 2014 ; CASoN 2014 : July 30-31, Porto, Portugal
n5:obor
n13:IN
n5:pocetDomacichTvurcuVysledku
1
n5:pocetTvurcuVysledku
6
n5:rokUplatneniVysledku
n10:2014
n5:tvurceVysledku
Lin, C. Jui, S.-L. Hassanien, A. E. Xiao., K. Abraham Padath, Ajith Guan, H.
n5:typAkce
n20:WRD
n5:zahajeniAkce
2014-07-30+02:00
s:numberOfPages
5
n9:doi
10.1109/NaBIC.2014.6921884
n17:hasPublisher
IEEE
n15:isbn
978-1-4799-5937-2
n14:organizacniJednotka
27740