Fuzzy shell-clustering and applications to circle detection in digital images

Research output: Contribution to journalArticlepeer-review

162 Scopus citations


A new type of Fuzzy Clustering algorithm called Fuzzy-Shell Clustering (FSC) is introduced, The FSC algorithm seeks cluster prototypes that are p-dimensional hyper-spherical-shells. In two-dimensional data, this amounts to finding cluster prototypes that are circles. Thus the FSC algorithm can be applied for detection of circles in digital images. The algorithm does not require the data-points to be in any particular order, therefore its performance can be compared with the global transformation techniques such as Hough transforms. Several numerical examples are considered and the performance of the FSC algorithm is compared to the performance of the methods based on generalized Hough transform (HT). The FSC is shown to be superior to the HT method with regards to memory requirement and computation time. Like the HT method, the FSC is successful even if only a part of a circular shape is present in the image. Other potential applications of FSC are also considered.

Original languageEnglish (US)
Pages (from-to)343-355
Number of pages13
JournalInternational Journal of General Systems
Issue number4
StatePublished - May 1990

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Information Systems
  • Control and Systems Engineering
  • Computer Science Applications
  • Modeling and Simulation


  • Cluster analysis
  • Hough transforms
  • circle detection
  • fuzzy clustering
  • image processing
  • pattern recognition


Dive into the research topics of 'Fuzzy shell-clustering and applications to circle detection in digital images'. Together they form a unique fingerprint.

Cite this