10. Fuzzy Set Theory in Image Processing

  1. Tinku Acharya and
  2. Ajoy K. Ray

Published Online: 20 SEP 2005

DOI: 10.1002/0471745790.ch10

Image Processing: Principles and Applications

Image Processing: Principles and Applications

How to Cite

Acharya, T. and Ray, A. K. (2005) Fuzzy Set Theory in Image Processing, in Image Processing: Principles and Applications, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/0471745790.ch10

Publication History

  1. Published Online: 20 SEP 2005
  2. Published Print: 19 AUG 2005

ISBN Information

Print ISBN: 9780471719984

Online ISBN: 9780471745792



  • vagueness;
  • membership function;
  • symmetric fuzzy membership;
  • fuzzy contrast;
  • fuzzy filter;
  • fuzzy histogram;
  • fuzzy MLP


A gray tone image possesses ambiguity within pixels because of the possible multi-valued levels of brightness in the image. This indeterminacy is due to the inherent vagueness or imprecision embedded in an image, which can be adequately modeled using fuzzy sets. The fundamentals of fuzzy set theory has been reviewed in this chapter. Fuzzy enhancement techniques, such as fuzzy contrast enhancement, has been presented in detail. Fuzzy spatial filters have found applications for noise removal - this has been discussed here. Fuzzy histogram modeling has been presented. A number of mage segmentation techniques by fuzzy methods have been described here. The chapter concludes with an introductory note on neuro-fuzzy techniques for image analysis.