Unsupervised Tissue Classification in Medical Images using Edge-Adaptive Clustering

Dzung L. Pham

Research output: Contribution to journalConference articlepeer-review

Abstract

A novel algorithm is proposed for performing unsupervised tissue classification in medical images by combining conventional clustering techniques with edge-adaptive segmentation techniques. Based on the fuzzy C-means algorithm, the algorithm computes a smooth segmentation while simultaneously estimating an edge field. Unlike most tissue classification algorithms that incorporate a smoothness constraint, the edge field estimation prevents the algorithm from smoothing across tissue boundaries, thereby producing robust yet accurate results. The algorithm is formulated as the minimization of an objective function that includes penalty terms to ensure that both the segmentation and edge field are relatively smooth. To compute the edge field, a difference equation with spatially varying coefficients is solved using an efficient multigrid algorithm. Some preliminary results applying the method to synthetic and magnetic resonance images are presented.

Original languageEnglish (US)
Pages (from-to)634-637
Number of pages4
JournalAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
Volume1
StatePublished - 2003
EventA New Beginning for Human Health: Proceedings of the 25th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Cancun, Mexico
Duration: Sep 17 2003Sep 21 2003

Keywords

  • Edge-adaptive
  • Fuzzy clustering
  • Image segmentation
  • Magnetic resonance imaging

ASJC Scopus subject areas

  • Signal Processing
  • Biomedical Engineering
  • Computer Vision and Pattern Recognition
  • Health Informatics

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