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Pattern Analysis & Applications

, Volume 2, Issue 1, pp 73–81 | Cite as

Algorithms for Fuzzy Segmentation

  • Bruno M. Carvalho
  • C. Joe Gau
  • Gabor T. Herman
  • T. Yung Kong
Original Article

Abstract

Fuzzy segmentation is an effective way of segmenting out objects in pictures containing both random noise and shading. This is illustrated both on mathematically created pictures and on some obtained from medical imaging. A theory of fuzzy segmentation is presented. To perform fuzzy segmentation, a ‘connectedness map’ needs to be produced. It is demonstrated that greedy algorithms for creating such a connectedness map are faster than the previously used dynamic programming technique. Once the connectedness map is created, segmentation is completed by a simple thresholding of the connectedness map. This approach is efficacious in instances where simple thresholding of the original picture fails.

Key words: Dynamic programming; Fuzzy pattern recognition; Greedy algorithms; Medical imaging; Segmentation; Thresholding 

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Copyright information

© Springer-Verlag London Limited 1999

Authors and Affiliations

  • Bruno M. Carvalho
    • 1
  • C. Joe Gau
    • 2
  • Gabor T. Herman
    • 1
  • T. Yung Kong
    • 3
  1. 1.MIPG, University of Pennsylvania, Philadelphia, PA, USAUSA
  2. 2.Graduate School & University Center, City University of New York, NY, USAUSA
  3. 3.Queens College, City University of New York, NY, USAUSA

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