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Fuzzy Filters for Image Processing pp 101–128Cite as

Fuzzy Based Image Segmentation

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 122))

Summary

In this chapter, we introduce some recently developed fuzzy based techniques for image segmentation. They are fuzzy thresholding, fuzzy rule-based inferencing scheme, fuzzy c-mean clustering, and fuzzy integral-based decision making. A fuzzy integral based region merging algorithm for image segmentation, which combines both region and edge features of the image, is then used to merge regions recursively according to the criterion of the maximum fuzzy integral. The region merging process is regarded as a nonlinear process that fuses objective evidence, in the form of a fuzzy membership function that reflects the similarities between adjacent regions with respect to each feature, with the apriori system’s expectation of the importance of that evidence provided by the corresponding feature. Using the maximum fuzzy integral criterion, the target number of regions can be reached by recursively merging regions. To handle the parameter initiation problem faced in the computation of fuzzy integral, an algorithm for automatically choosing such parameters is formulated as an optimization problem. A simulated annealing algorithm is designed to explore the value space of fuzzy densities and search the (near) optimal solution corresponding to a minimum cost value. This way, fuzzy densities are determined adaptively depending on the image at hand without requiring any human intervention. To evaluate the performance of the proposed approach, it is applied to magnetic resonance images (MRI) and natural images. The experimental results have demonstrated that the proposed approach brings out robust segmentation performance and outperforms other approaches efficiently.

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Author information

Authors and Affiliations

  1. Department of Systems Design Engineering, University of Waterloo, Waterloo, ON, Canada, N2L 3G1

    Otman Basir, Hongwei Zhu & Fakhri Karray

Authors
  1. Otman Basir
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  2. Hongwei Zhu
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  3. Fakhri Karray
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Editor information

Editors and Affiliations

  1. Dept. of Applied Mathematics and Computer Science, Ghent University, Krijgslaan 281 (S9), 9000, Gent, Belgium

    Mike Nachtegael , Dietrich Van der Weken  & Etienne E. Kerre ,  & 

  2. Biomedical Imaging Group EPFL/STI/LIB, Swiss Federal Institute of Technology Lausanne, Bat. BM4. 140, 1015, Lausanne EPFL, Switzerland

    Dimitri Van De Ville

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© 2003 Springer-Verlag Berlin Heidelberg

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Basir, O., Zhu, H., Karray, F. (2003). Fuzzy Based Image Segmentation. In: Nachtegael, M., Van der Weken, D., Kerre, E.E., Van De Ville, D. (eds) Fuzzy Filters for Image Processing. Studies in Fuzziness and Soft Computing, vol 122. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-36420-7_5

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  • DOI: https://doi.org/10.1007/978-3-540-36420-7_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-05591-1

  • Online ISBN: 978-3-540-36420-7

  • eBook Packages: Springer Book Archive

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