On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system

Author

Tolias, Yannis A. ; Panas, Stavros M.

Author_Institution

Signal Process. & Biomed. Technol. Unit, Aristotelian Univ. of Thessaloniki, Greece

Volume

5

Issue

10

fYear

1998

Firstpage

245

Lastpage

247

Abstract

A novel approach for enhancing the results of fuzzy clustering by imposing spatial constraints for solving image segmentation problems is presented. We have developed a Sugeno (185) type rule-based system with three inputs and 11 rules that interacts with the clustering results obtained by the well-known fuzzy c-means (FCM) and/or possibilistic c-means (PCM) algorithms. It provides good image segmentations in terms of region smoothness and elimination of the effects of noise. The results of the proposed rule-based neighborhood enhancement (RB-NE) system are compared to well-known segmentation algorithms using stochastic field modeling. They are found to be of comparable quality, while being of lower computational complexity.

Keywords

computational complexity; correlation methods; fuzzy systems; image enhancement; image segmentation; knowledge based systems; noise; pattern recognition; Sugeno-type rule-based system; computational complexity; correlation; fuzzy c-means algorithm; fuzzy image clustering; fuzzy rule-based system; image segmentation; noise effects elimination; possibilistic c-means algorithm; region smoothness; rule-based neighborhood enhancement system; segmentation algorithms; spatial constraints; stochastic field modeling; Clustering algorithms; Fuzzy sets; Fuzzy systems; Image segmentation; Knowledge based systems; Partitioning algorithms; Phase change materials; Prototypes; Signal processing algorithms; Voting;

fLanguage

English

Journal_Title

Signal Processing Letters, IEEE

Publisher

ieee

ISSN

1070-9908

Type

jour

DOI

10.1109/97.720555

Filename

720555