Integrating intensity and boundary information for tissue classification

Dzung L. Pham

Research output: Contribution to journalConference articlepeer-review

Abstract

A new algorithm is proposed for performing unsupervised tissue classification in medical images by integrating 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.

Original languageEnglish (US)
Pages (from-to)216-220
Number of pages5
JournalProceedings of the IEEE Symposium on Computer-Based Medical Systems
Volume17
StatePublished - Sep 29 2004
EventProceedings 17th IEEE Symposium on Computer-Based Medical Systems, CBMS 2004 - Bethesda, MD, United States
Duration: Jun 24 2004Jun 25 2004

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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