Spatial models for fuzzy clustering

Dzung L. Pham

Research output: Contribution to journalArticle

Abstract

A novel approach to fuzzy clustering for image segmentation is described. The fuzzy C-means objective function is generalized to include a spatial penalty on the membership functions. The penalty term leads to an iterative algorithm that is only slightly different from the original fuzzy C-means algorithm and allows the estimation of spatially smooth membership functions. To determine the strength of the penalty function, a criterion based on cross-validation is employed. The new algorithm is applied to simulated and real magnetic resonance images and is shown to be more robust to noise and other artifacts than competing approaches.

Original languageEnglish (US)
Pages (from-to)285-297
Number of pages13
JournalComputer Vision and Image Understanding
Volume84
Issue number2
DOIs
StatePublished - Nov 2002

Fingerprint

Fuzzy clustering
Membership functions
Magnetic resonance
Image segmentation

Keywords

  • Cross-validation
  • Fuzzy c-means
  • Fuzzy clustering
  • Image segmentation
  • Markov random fields

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Signal Processing
  • Electrical and Electronic Engineering

Cite this

Spatial models for fuzzy clustering. / Pham, Dzung L.

In: Computer Vision and Image Understanding, Vol. 84, No. 2, 11.2002, p. 285-297.

Research output: Contribution to journalArticle

Pham, Dzung L. / Spatial models for fuzzy clustering. In: Computer Vision and Image Understanding. 2002 ; Vol. 84, No. 2. pp. 285-297.
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