An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities

Dzung L. Pham, Jerry L. Prince

Research output: Contribution to journalArticlepeer-review

Abstract

We present a novel algorithm for obtaining fuzzy segmentations of images that are subject to multiplicative intensity inhomogeneities, such as magnetic resonance images. The algorithm is formulated by modifying the objective function in the fuzzy C-means algorithm to include a multiplier field, which allows the centroids for each class to vary across the image. First and second order regularization terms ensure that the multiplier field is both slowly varying and smooth. An iterative algorithm that minimizes the objective function is described, and its efficacy is demonstrated on several test images.

Original languageEnglish (US)
Pages (from-to)57-68
Number of pages12
JournalPattern Recognition Letters
Volume20
Issue number1
DOIs
StatePublished - Jan 1999

Keywords

  • Fuzzy C-means
  • Image segmentation
  • Intensity inhomogeneities
  • Magnetic resonance imaging

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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