N-cuts parameter adjustment using evolving fuzzy inferencing

Ahmed A. Othman, Hamid R. Tizhoosh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Normalized cut (N-cut) is a rather recent approach to image segmentation representing the image as a graph and using eigenvalues to partition it. However, this method has several parameters that affect the segmentation accuracy. Using pre-set values for these parameters may generate good results for some images and bad results for others. Thus, to achieve maximum segmentation accuracy, these parameters may be manually fine-tuned for every set of images. This process, of course, would be impractical and lack generality. In this paper, a method is proposed to automatically determine N-cut parameters for every single image based on the image features using evolving fuzzy sets. The proposed method is applied to magnetic reasoning images (MRI) of bladder.

Original languageEnglish (US)
Title of host publicationFUZZ-IEEE 2013 - 2013 IEEE International Conference on Fuzzy Systems
DOIs
StatePublished - 2013
Event2013 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2013 - Hyderabad, India
Duration: Jul 7 2013Jul 10 2013

Publication series

NameIEEE International Conference on Fuzzy Systems
ISSN (Print)1098-7584

Conference

Conference2013 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2013
Country/TerritoryIndia
CityHyderabad
Period7/7/137/10/13

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics

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