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  • 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. (en)
Title
  • Fuzzy c-means with wavelet filtration for MR image segmentation
  • Fuzzy c-means with wavelet filtration for MR image segmentation (en)
skos:prefLabel
  • Fuzzy c-means with wavelet filtration for MR image segmentation
  • Fuzzy c-means with wavelet filtration for MR image segmentation (en)
skos:notation
  • RIV/61989100:27740/14:86092828!RIV15-MSM-27740___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • S
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
  • Abraham Padath, Ajith
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 17816
http://linked.open...ai/riv/idVysledku
  • RIV/61989100:27740/14:86092828
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • wavelet; segmentation; noise reduction; MR image; fuzzy c-means; clustering (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [E0403E575820]
http://linked.open...v/mistoKonaniAkce
  • Porto
http://linked.open...i/riv/mistoVydani
  • New York
http://linked.open...i/riv/nazevZdroje
  • NaBIC 2014 ; CASoN 2014 : July 30-31, Porto, Portugal
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Abraham Padath, Ajith
  • Hassanien, A. E.
  • Guan, H.
  • Jui, S.-L.
  • Lin, C.
  • Xiao., K.
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
number of pages
http://bibframe.org/vocab/doi
  • 10.1109/NaBIC.2014.6921884
http://purl.org/ne...btex#hasPublisher
  • IEEE
https://schema.org/isbn
  • 978-1-4799-5937-2
http://localhost/t...ganizacniJednotka
  • 27740
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