Generalized fuzzy clustering for segmentation of multi-spectral magnetic resonance images

Renjie He, Sushmita Datta, Balasrinivasa Rao Sajja, Ponnada A. Narayana

Research output: Contribution to journalArticlepeer-review

30 Scopus citations


An integrated approach for multi-spectral segmentation of MR images is presented. This method is based on the fuzzy c-means (FCM) and includes bias field correction and contextual constraints over spatial intensity distribution and accounts for the non-spherical cluster's shape in the feature space. The bias field is modeled as a linear combination of smooth polynomial basis functions for fast computation in the clustering iterations. Regularization terms for the neighborhood continuity of intensity are added into the FCM cost functions. To reduce the computational complexity, the contextual regularizations are separated from the clustering iterations. Since the feature space is not isotropic, distance measure adopted in Gustafson-Kessel (G-K) algorithm is used instead of the Euclidean distance, to account for the non-spherical shape of the clusters in the feature space. These algorithms are quantitatively evaluated on MR brain images using the similarity measures.

Original languageEnglish (US)
Pages (from-to)353-366
Number of pages14
JournalComputerized Medical Imaging and Graphics
Issue number5
StatePublished - Jul 2008
Externally publishedYes


  • Adaptive FCM
  • Contextual constraints
  • G-K algorithm
  • Inhomogeneity field
  • MRI
  • Multi-spectral segmentation
  • Similarity measures

ASJC Scopus subject areas

  • Radiological and Ultrasound Technology
  • Radiology Nuclear Medicine and imaging
  • Computer Vision and Pattern Recognition
  • Health Informatics
  • Computer Graphics and Computer-Aided Design


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