Skip to main content
SpringerLink
Log in

Analysis of Human Bone Disorder Using Fuzzy and Possibility Theory

  • Chapter
  • First Online:
Hybrid Machine Intelligence for Medical Image Analysis

Part of the book series: Studies in Computational Intelligence ((SCI,volume 841))

  • 950 Accesses

Abstract

X-ray image-based pre-diagnosis modality is the cheapest way of dealing any bone-related problems. Identification of the particular region eliminating bones from the flesh and cartilage in the X-ray image where bone disorder may occur is the objective of this work to facilitate the doctors for proper diagnosis. We employ possibility theory for analyzing the X-ray images to appraise the possibility of the regions having the disorder. The novelty of the work is to facilitate the doctors concentrating only on automatic detection of region of interest (ROI) for correct diagnosis of the patients. The projected technique consists the steps—(i) preprocessing to denoise the X-ray image using fuzzy inference system, (ii) isolation of the bone region from the rest of the X-ray image using Type 1 and Type 2 fuzzy-based edge discovery methods, and (iii) in order to enable the experts for more precise diagnosis, region of interest (ROI) of the bone has been identified using possibility theory. Finally, experimental results of different bone regions using the proposed approach have been demonstrated and compared with the existing methods showing better performance and diagnosis.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Bustince, H., Burillo, P.: Mathematical analysis of interval-valued fuzzy relations: application to approximate reasoning. Fuzzy Sets Syst. 113, 205–219 (2000)

    Article  MathSciNet  Google Scholar 

  2. Chaneau, J.L., Gunaratne, M., Altschaeffl, A.G.: An application of type-2 sets to decision making in engineering. In: Bezdek, J. (ed.) Analysis of Fuzzy Information, vol. II: Artificial Intelligence and Decision Systems. CRC Press, Boca Raton, FL (1987)

    Google Scholar 

  3. Bounhas, M., Mellouli, K., Prade, H., Serrurier, M.: From Bayesian classifiers to possibilistic classifiers for numerical data. In: Deshpande, A., Hunter, A. (eds.) SUM 2010, LNAI 6379, pp. 112–125, Springer, Heidelberg (2010)

    Google Scholar 

  4. Dempster, A.P.: Upper and lower probabilities induced by a multivalued mapping. Ann. Math. Stat. 38(2), 325–339 (1967)

    Article  MathSciNet  Google Scholar 

  5. Operations in a fuzzy-valued logic. Inform. Control 43, 224–240 (1979)

    Google Scholar 

  6. Fuzzy Sets and Systems: Theory and Applications. Academic, New York (1980)

    Google Scholar 

  7. Dubois, D., Prade, H.: Possibility theory as a basis for preference propagation in automated reasoning. In: Proceedings of the IEEE International Conference on Fuzzy Systems, pp. 821–832. San Diego (1992)

    Google Scholar 

  8. Dubois, D., Prade, H.: On possibility/probability transformations. Fuzzy Logic, 103–112 (1993)

    Google Scholar 

  9. Izumi, K., Tanaka, H., Asai, K.: Resolution of composite fuzzy relational equations of type 2. Trans. Inst. Electr. Common. Engineers Japan, pt. D J66D, 1107–1113 (1983) (in Japanese)

    Google Scholar 

  10. John, R.I.: Type-2 inferencing and community transport scheduling. In: Proceedings of the 4th European Congress Intelligent Techniques Soft Computing, pp. 1369–1372. Aachen, Germany (1996)

    Google Scholar 

  11. Farbiz, F., Menhaj, M.B., Motamedi, S.A.: Edge preserving image filtering based on fuzz logic. In: Proceedings of the 6th EUFIT Conference, pp. 1417–1421 (1998)

    Google Scholar 

  12. Kwan, H.K., Cai, Y.: Fuzzy filters for image filtering. In: Proceedings of Circuits and Systems (MWSCAS-2002) (2002)

    Google Scholar 

  13. Tolt, G., Kalaykov, I.: Fuzzy-similarity-based image noise cancellation. In: Lecture Notes in Computer Science, vol. 2275, pp. 408–413 (2002)

    Chapter  Google Scholar 

  14. Balster, E.J., Zheng, Y.F., Ewing, R.L.: Feature-based wavelet shrinkage algorithm for image denoising. IEEE Trans. Image Proc. 14(3), 2024–2039 (2005)

    Article  Google Scholar 

  15. Type-2 fuzzy logic systems: Type-reduction. In: Proceedings of the IEEE Conference on Systems, Man and Cybernetics, pp. 2046–2051 (1998)

    Google Scholar 

  16. Vertan, C., Buzuloiu, V.: Fuzzy nonlinear filtering of Colour images. In: Fuzzy Techniques in Image Processing (Vol. 52 of Studies in Fuzz. and Soft Comp.), pp. 248–264 (2000)

    Chapter  Google Scholar 

  17. Vermandel, M., Betrouni, N., Taschner, C., Vasseur, C., Rousseau, J.: From MIP image to MRA segmentation using fuzzy set theory. Comput. Med. Imag. Graph. 31, 128–140 (2007)

    Article  Google Scholar 

  18. Xu, H., Zhu, G., Peng, H., Wang, D.: Adaptive fuzzy switching filter for images corrupted by impulse noise. In: Pattern Recognition Letters, vol. 25, pp. 1657–1663 (2004)

    Article  Google Scholar 

  19. Schulte, S., De Witte, V., Nachtegael, M., Mélange, T., Kerre, E.E.: A new fuzzy additive noise reduction method. In: Lecture Notes in Computer Science, vol. 4633 (Proc. of ICIAR 2007), pp. 12–23 (2007)

    Google Scholar 

  20. Lukac, R.: Adaptive vector median filtering. In: Pattern Recognition Letters, vol. 24, pp. 1889–1899 (2003)

    Article  Google Scholar 

  21. Romberg, J.K., Choi, H., Baraniuk, R.G.: Bayesian tree-structured image modelling using wavelet-domain hidden Markov models. IEEE Trans. Image Proc. 10(7), 1056–1068 (2001)

    Article  Google Scholar 

  22. Dabov, K., Foi, A., Katkovnik, V., Egiazarian, K.: Image denoising with block-matching and 3D filtering. In: Proceedings of SPIE Electronic Imaging, pp. 354–365 (2006)

    Google Scholar 

  23. Jentzen, W., Freudenberg, L., Eising, E.G., Heinze, M., Brandau, W., Bockisch, A.: Segmentation of PET volumes by iterative image thresholding. J. Nucl. Med. 48(1), 108–114 (2007)

    Google Scholar 

  24. Alsahwa, B., Solaiman, B., Almouahed, S., Bossé, É., Guériot, D.: Iterative refinement of possibility distributions by learning for pixel-based classification. IEEE Trans. Image Process. 25(8), 3533–3545

    Article  MathSciNet  Google Scholar 

  25. Zadeh, L.A.: Fuzzy sets as the basis for a theory of possibility. Fuzzy Sets Syst. 1, 3–28 (1978)

    Article  MathSciNet  Google Scholar 

  26. Maity, S., Sil, J.: Feature extraction of bone scintigraphy for diagnosis of disease—CSNT 2012, IEEE Explore, pp. 179–183. https://doi.org/10.1109/csnt.2012.46

  27. Bezdek, J.C.: Pattern recognition with fuzzy objective function algorithms. Plenum Press, New York (1981)

    Chapter  Google Scholar 

  28. Vial, S., Gibon, D., Vasseur, C., Rousseau, J.: Volume delineation by fusion of fuzzy sets obtained from multiplanar tomographic images. IEEE Trans. Med. Imag. 20, 1362–1372 (2001)

    Article  Google Scholar 

  29. Schaefer, A., Kremp, S., Hellwig, D., Rube, C., Kirsch, C.M., Nestle, U.: A contrast-oriented algorithm for FDG-PET-based delineation of tumour volumes for the radiotherapy of lung cancer: derivation from phantom measurements and validation in patient data. Eur. J. Nucl. Med. Mol. Imag. 35(11), 1989–1999 (2008)

    Article  Google Scholar 

  30. Zadeh, L.A.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  Google Scholar 

  31. Christensen, G.E., Johnson, H.J.: Consistent image registration. IEEE Trans. Med. Imag. 20(7), 568–582 (2001)

    Article  Google Scholar 

Download references

Acknowledgements

I would like to thank the management of Mission Hospital for giving me the opportunity to work and share the digital images for my research work. I also would like to thank Dr. Jaya Sil of IIEST for her continuous guidance and support.

Author information

Authors and Affiliations

  1. JIS University, Kolkata, 700109, India

    Saikat Maity

  2. Indian Institute Engineering Science & Technology, Shibpur, 711103, India

    Jaya Sil

Authors
  1. Saikat Maity
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jaya Sil
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Saikat Maity .

Editor information

Editors and Affiliations

  1. Department of Information Technology, RCC Institute of Information Technology, Beliaghata, West Bengal, India

    Siddhartha Bhattacharyya

  2. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Gangtok, Sikkim, India

    Debanjan Konar

  3. Department of Computer Science, VSB-Technical University of Ostrava, Ostrava-Poruba, Czech Republic

    Jan Platos

  4. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Gangtok, Sikkim, India

    Chinmoy Kar

  5. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology, Gangtok, Sikkim, India

    Kalpana Sharma

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Maity, S., Sil, J. (2020). Analysis of Human Bone Disorder Using Fuzzy and Possibility Theory. In: Bhattacharyya, S., Konar, D., Platos, J., Kar, C., Sharma, K. (eds) Hybrid Machine Intelligence for Medical Image Analysis. Studies in Computational Intelligence, vol 841. Springer, Singapore. https://doi.org/10.1007/978-981-13-8930-6_6

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-8930-6_6

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-8929-0

  • Online ISBN: 978-981-13-8930-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation