Advertisement

Performance of Optimized Fuzzy Edge Detectors using Particle Swarm Algorithm

  • Noor Elaiza Abdul Khalid
  • Mazani Manaf
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6145)

Abstract

The purpose of the paper is to compare the performance of various fuzzy edge detectors which have been optimized by Particle Swarm Optimization (PSO). Three different type edge detectors Classical fuzzy Heuristic (CFH), Gaussian rule based (GRBF) and Robust Fuzzy Complement (RFC) are used. These edge detectors are effective in detecting edges, however the edges are thick. This paper proposes the used of particle swarm optimization algorithm as a method of producing thin and measurable edges. The fuzzy edge detectors are used in the initial swarm population and the objective function. The performance is based on the consistency of the visual appearance, fuzzy membership threshold and the number of complete edges detected. All three optimized edge detector performs reasonably well but CFHPSO outperform the rest.

Keywords

Fuzzy edge detection Particle swarm optimization Stochastic Hybrid 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    O’Donnell, L.: Semi-Automatic Medical Image Segmentation. Department of Electrical Engineering and Computer Science, Thesis, Massachusetts Institute of Technology (2001)Google Scholar
  2. 2.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 2nd edn. Addison Wesley, Reading (2002)Google Scholar
  3. 3.
    Lasaygues, P., et al.: Progress Towards in Vitro Quantitative Imaging of Human Femur Using Compound Quantitative Ultrasonic Tomography. Physics in Medicine and Biology 50, 263–2649 (2005)CrossRefGoogle Scholar
  4. 4.
    Thodberg, H.H., Rosholm, A.: Application of the Active Shape Model in a Comercial Medical Device for Bone Densitometry. Image and Vision Computing 21, 1155–1161 (2003)CrossRefGoogle Scholar
  5. 5.
    Tizhoosh, H.R.: Fast fuzzy Edge Detection. In: Proceedings of Fuzzy Information Processing Society, NAFIPS, 2002 Annual Meeting of the North American, pp. 239–242 (2002)Google Scholar
  6. 6.
    Eberhart, R.C., Shi, Y.: Particle Swarm Optimization: Developments, Applications and Resources. In: Proceedings of Congress on Evolutionary Computation. IEEE Service Center, NJ (2001)Google Scholar
  7. 7.
    Khalid, N.E.A., Manaf, M., Aziz, M.E.: Modified Fuzzy Heuristics Edge Detection – Tubular Bone Boundary Detection. NSFTA (2008)Google Scholar
  8. 8.
    Kareem, A., Shuaib, L.I., Lee, C.W., Aziz, M.E.: The Determination of Local Malay Female Bone Mineral Density and Its Correlation With Geometric Properties in The Evaluation of Skeletal Status. In: Lights of Medicine 9th National Conference on Medical Sciences (2004)Google Scholar
  9. 9.
    Khalid, N.E.A., et al.: CR Images of Metacarpel Cortical Edge Detection-Bone Profile Histogram Approximation Method. In: Intelligent and Advanced Systems, ICIAS 2007, November 25-28, pp. 702–708 (2007)Google Scholar
  10. 10.
    Khalid, N.E.A., et al.: Gaussian Rule Based Fuzzy (GRBF) Membership Edge Detection on Hand Phantom Radiograph Images. In: Fifth International Conference on Computer Graphics, Imaging and Visualisation, CGIV 2008, August 26-28, pp. 265–270 (2008)Google Scholar
  11. 11.
    Eberhart, R.C., Kennedy, J.: A New Optimizer Using Particle Swarm Theory. In: Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan, pp. 39–43 (1995)Google Scholar
  12. 12.
    Omran, M.G., Engelbrecht, P.A., Salman, A.: A Color Image Quantization Algorithm Based on Particle Swarm Optimization. Informatica 29, 261–269 (2005)zbMATHGoogle Scholar
  13. 13.
    Kennedy, J.: The Particle Swarm: Social Adaptation of Knowledge. In: IEEE International Conference on Evolutionary Computation, pp. 303–308 (1997)Google Scholar
  14. 14.
    Carlisle, A., Dozier, G.: Adapting Particle Swam Optimization to Dynamic Environments. In: The Proceedings of the 2000 International Conference on Artificial Intelligence, pp. 429–433 (2000)Google Scholar
  15. 15.
    Bergh, V.D., Engelbrecht, A.: Effects of Swarm Size on Cooperative Particle Swarm Optimisers. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO 2001, pp. 892–899 (2001)Google Scholar
  16. 16.
    Suganthan, P.: Particle Swarm Optimiser with Neighborhood Operator. In: Proceedings of the 1999 Congress on Evolutionary Computation, pp. 1958–1962 (1999)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Noor Elaiza Abdul Khalid
    • 1
  • Mazani Manaf
    • 1
  1. 1.Faculty of Computer and Mathematical SciencesUniversiti Teknologi MARA Shah AlamSelangorMalaysia

Personalised recommendations