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 journalConference articlepeer-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)555-563
Number of pages9
JournalProceedings of SPIE - The International Society for Optical Engineering
Volume3338
DOIs
StatePublished - Dec 1 1998
EventMedical Imaging 1998: Image Processing - San Diego, CA, United States
Duration: Feb 23 1998Feb 23 1998

Keywords

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

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

Fingerprint Dive into the research topics of 'An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities'. Together they form a unique fingerprint.

Cite this