Fuzzy c-means variants for medical image segmentation

Huiyu Zhou*, Gerald Schaefer

*Corresponding author for this work

Research output: Contribution to journalSpecial issue

Abstract

Segmentation is an important step in many medical imaging applications and a variety of image segmentation techniques exist. One group of segmentation algorithms is based on clustering concepts. In this article we investigate several fuzzy c-means based clustering algorithms and their application to medical image segmentation. In particular we evaluate the conventional hard c-means (HCM) and fuzzy c-means (FCM) approaches as well as three computationally more efficient derivatives of fuzzy c-means: fast FCM with random sampling, fast generalised FCM, and a new anisotropic mean shift based FCM.

Original languageEnglish
Pages (from-to)3-18
Number of pages16
JournalInternational Journal of Tomography and Statistics
Volume13
Issue numberW10
Publication statusPublished - 31 Aug 2010

Fingerprint

Image segmentation
Cluster Analysis
Medical imaging
Diagnostic Imaging
Clustering algorithms
Sampling
Derivatives

Keywords

  • Clustering
  • Fuzzy c-means
  • Medical image segmentation

Cite this

Zhou, Huiyu ; Schaefer, Gerald. / Fuzzy c-means variants for medical image segmentation. In: International Journal of Tomography and Statistics. 2010 ; Vol. 13, No. W10. pp. 3-18.
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Fuzzy c-means variants for medical image segmentation. / Zhou, Huiyu; Schaefer, Gerald.

In: International Journal of Tomography and Statistics, Vol. 13, No. W10, 31.08.2010, p. 3-18.

Research output: Contribution to journalSpecial issue

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AU - Schaefer, Gerald

PY - 2010/8/31

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