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A novel fuzzy evidential reasoning paradigm for data fusion with applications in image processing

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Abstract

This paper presents a novel data fusion paradigm based on fuzzy evidential reasoning. A new fuzzy evidence structure model is first introduced to formulate probabilistic evidence and fuzzy evidence in a unified framework. A generalized Dempster’s rule is then utilized to combine fuzzy evidence structures associated with multiple information sources. Finally, an effective decision rule is developed to take into account uncertainty, quantified by Shannon entropy and fuzzy entropy, of probabilistic evidence and fuzzy evidence, to deal with conflict and to achieve robust decisions. To demonstrate the effectiveness of the proposed paradigm, we apply it to classifying synthetic images and segmenting multi-modality human brain MR images. It is concluded that the proposed paradigm outperforms both the traditional Dempster–Shafer evidence theory based approach and the fuzzy reasoning based approach

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References

  1. Le Hegarat-Mascle S, Bloch I, Vidal-Madjar D (1997) Application of Dempster–Shafer evidence theory to unsupervised classification in multisource remote sensing. IEEE Tran Geosci Remote Sens 35:1018–1031

    Article  Google Scholar 

  2. Le Hegarat-Mascle S, Bloch I, Vidal-Madjar D (1998) Introduction of neighborhood information in evidence theory and application to data fusion of radar and optical images with partial cloud cover. Pattern Recogn 31:1811–1823

    Article  Google Scholar 

  3. Bloch I (1996) Some aspects of Dempster–Shafer evidence theory for classification of multi-modality medical images taking partial volume effect into account. Pattern Recogn Lett 17:905–916

    Article  Google Scholar 

  4. Rombaut M, Zhu YM (2002) Study of Dempster–Shafer for image segmentation applications. Image Vis Comput 20:15–23

    Article  Google Scholar 

  5. Basir OA, Shen HC (1999) Interdependence and information loss in multi-sensor system. J Robot Syst 16:597–612

    Article  MATH  Google Scholar 

  6. Shafer G (1976) A mathematical theory of evidence. Princeton University Press, Priceton, NJ, USA

    MATH  Google Scholar 

  7. Yager T (1982) Generalized probabilities of fuzzy events from fuzzy belief structures. Inf Sci 28:45–62

    Article  MATH  MathSciNet  Google Scholar 

  8. Yager RR, Filev DP (1995) Including probabilistic uncertainty in fuzzy logic controller modelling using Dempster–Shafer theory. IEEE Trans SMC 25:1221–1230

    Google Scholar 

  9. Yen J (1990) Generalizing the Dempster–Shafer theory to fuzzy sets. IEEE Trans SMC 20:559–570

    MATH  MathSciNet  Google Scholar 

  10. Denoeux T, Zouhal LM (2001) Handling possibilistic labels in pattern classification using evidential reasoning. Fuzzy Sets Syst 122:409–424

    Article  MATH  MathSciNet  Google Scholar 

  11. Smets P, Kennes R (1994) The transferable belief model. Artif Intell 66:191–234

    Article  MATH  MathSciNet  Google Scholar 

  12. Mahler RP (1995) Combining ambiguous evidence with respect to ambiguous a priori knolwedge part II: fuzzy logic. Fuzzy Sets Syst 75:319–354

    Article  MATH  MathSciNet  Google Scholar 

  13. Ishizuka M, Fu KS, Tao JTP (1982) Inference procedures and uncertainty for the problem-reduction method. Inf Sci 28:179–206

    Article  MATH  Google Scholar 

  14. Ogawa H, Fu KD (1985) An inexact inference for damage assessment of existing structures. Int J Man Mach Stud 22:295–306

    Article  Google Scholar 

  15. Binaghi E, Madella P (1999) Fuzzy Dempster–Shafer reasoning for rule-based classifiers. Int J Intell Syst 14:559–583

    Article  MATH  Google Scholar 

  16. Dubois D, Prade H (1982) On several representations of an uncertainty body of evidence. In: Gupta MM, Sanchz E (eds). Fuzzy informaton and decision processes. North-Holland, New York, pp 167–181

    Google Scholar 

  17. Zhu H, Basir O (2003) A scheme for constructing evidence structures in Dempster–Shafer evidence theory for data fusion. In: Proceedings of the 5th IEEE international symposium on computational intelligence in robotics and automation, Japan, vol 2, pp 960–965

  18. Basir O, Karray F, Zhu H (2005) Connectionist based Dempster–Shafer evidential reasoning for data fusion. IEEE Trans Neural Network 16:1513–1530

    Article  Google Scholar 

  19. Shang X, Jiang W (1997) A note on fuzzy information measures. Pattern Recogn Lett 18:425–432

    Article  Google Scholar 

  20. De Luca A, Termini S (1972) A definition of non probabilistic entropy in the setting of fuzzy set theory. Inf Control 20:301–312

    Article  MathSciNet  MATH  Google Scholar 

  21. Collins DL, Zijdenbos AP, Kollokian V, Sled JG, Kabani NJ, CJ Holmes, AC Evans (1998) Design and construction of a pealistic digital brain phantom. IEEE Trans Med Imaging 17:463–468

    Article  Google Scholar 

  22. Simulated Brain Database: http://wwwbicmnimcgillca/brainweb/

  23. Duda RO, Hart PE, Stork DG (2001) Pattern classification. Wiley, Chichester, New York

    MATH  Google Scholar 

  24. Zhu H, Basir O (2003) Feature region-merging based fuzzy rules extraction for pattern classification. In: Proceedings IEEE international conference on fuzzy systems, St. Louis, USA, pp 696–701

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Authors and Affiliations

  1. PAMI Research Group, E & CE, University of Waterloo, 200 University Ave. West, Waterloo, ON, N2L 3G1, Canada

    H. Zhu

  2. Department of Electrical and Computer Engineering, University of Waterloo, 200 University Ave. West, Waterloo, ON, N2L 3G1, Canada

    O. Basir

Authors
  1. H. Zhu
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  2. O. Basir
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Correspondence to H. Zhu.

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Zhu, H., Basir, O. A novel fuzzy evidential reasoning paradigm for data fusion with applications in image processing. Soft Comput 10, 1169–1180 (2006). https://doi.org/10.1007/s00500-005-0039-1

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  • DOI: https://doi.org/10.1007/s00500-005-0039-1

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