A scalable framework for segmenting magnetic resonance images

Prodip Hore, Lawrence O. Hall, Dmitry B. Goldgof, Yuhua Gu, Andrew A. Maudsley, Ammar Darkazanli

Research output: Contribution to journalArticlepeer-review

62 Scopus citations


A fast, accurate and fully automatic method of segmenting magnetic resonance images of the human brain is introduced. The approach scales well allowing fast segmentations of fine resolution images. The approach is based on modifications of the soft clustering algorithm, fuzzy c-means, that enable it to scale to large data sets. Two types of modifications to create incremental versions of fuzzy c-means are discussed. They are much faster when compared to fuzzy c-means for medium to extremely large data sets because they work on successive subsets of the data. They are comparable in quality to application of fuzzy c-means to all of the data. The clustering algorithms coupled with inhomogeneity correction and smoothing are used to create a framework for automatically segmenting magnetic resonance images of the human brain. The framework is applied to a set of normal human brain volumes acquired from different magnetic resonance scanners using different head coils, acquisition parameters and field strengths. Results are compared to those from two widely used magnetic resonance image segmentation programs, Statistical Parametric Mapping and the FMRIB Software Library (FSL). The results are comparable to FSL while providing significant speed-up and better scalability to larger volumes of data.

Original languageEnglish (US)
Pages (from-to)183-203
Number of pages21
JournalJournal of Signal Processing Systems
Issue number1-3
StatePublished - Jan 2009


  • Automatic
  • Clustering
  • FCM
  • Magnetic resonance imaging
  • Scalable
  • Segmentation

ASJC Scopus subject areas

  • Hardware and Architecture
  • Information Systems
  • Signal Processing
  • Theoretical Computer Science
  • Control and Systems Engineering
  • Modeling and Simulation


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