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  • This paper presents a Fuzzy C-Means based image segmentation approach with an optimum threshold using measure of fuzziness. The optimized version, herein denoted as FCM-t, benefits from an optimum threshold, calculated using measure of fuzziness. This allows the revealing of ambiguous pixels, which are eventually assigned to the appropriate clusters by calculating the rounded average cluster values in the ambiguous pixels neighbourhood. The proposed approach showed significantly better results compared to the traditional Fuzzy C-Means, at the cost of some processing power. By benefiting from the optimum threshold approach, one is able to increase the segmentation performance by approximately three times more than with the traditional FCM. Springer International Publishing Switzerland 2014.
  • This paper presents a Fuzzy C-Means based image segmentation approach with an optimum threshold using measure of fuzziness. The optimized version, herein denoted as FCM-t, benefits from an optimum threshold, calculated using measure of fuzziness. This allows the revealing of ambiguous pixels, which are eventually assigned to the appropriate clusters by calculating the rounded average cluster values in the ambiguous pixels neighbourhood. The proposed approach showed significantly better results compared to the traditional Fuzzy C-Means, at the cost of some processing power. By benefiting from the optimum threshold approach, one is able to increase the segmentation performance by approximately three times more than with the traditional FCM. Springer International Publishing Switzerland 2014. (en)
Title
  • Liver CT Image Segmentation with an Optimum Threshold Using Measure of Fuzziness
  • Liver CT Image Segmentation with an Optimum Threshold Using Measure of Fuzziness (en)
skos:prefLabel
  • Liver CT Image Segmentation with an Optimum Threshold Using Measure of Fuzziness
  • Liver CT Image Segmentation with an Optimum Threshold Using Measure of Fuzziness (en)
skos:notation
  • RIV/61989100:27740/14:86092551!RIV15-MSM-27740___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(ED1.1.00/02.0070), P(EE.2.3.20.0073)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 26413
http://linked.open...ai/riv/idVysledku
  • RIV/61989100:27740/14:86092551
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • threshold; Segmentation; fuzzy C-means; clustering (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [D07C4DFC130A]
http://linked.open...v/mistoKonaniAkce
  • Ostrava
http://linked.open...i/riv/mistoVydani
  • Berlin Heidelberg
http://linked.open...i/riv/nazevZdroje
  • Advances in Intelligent Systems and Computing. Volume 303
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Hassanien, A. E.
  • Snášel, Václav
  • Tolba, M. F.
  • Ali, A.-R.
  • Couceiro, M.
  • Anter, A. M.
http://linked.open...vavai/riv/typAkce
http://linked.open.../riv/zahajeniAkce
issn
  • 2194-5357
number of pages
http://bibframe.org/vocab/doi
  • 10.1007/978-3-319-08156-4_9
http://purl.org/ne...btex#hasPublisher
  • Springer-Verlag. (Berlin; Heidelberg)
https://schema.org/isbn
  • 978-3-319-08155-7
http://localhost/t...ganizacniJednotka
  • 27740
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