Skip to main content
SpringerLink
Log in

Extraction of Texture Features from X-Ray Images: Case of Osteoarthritis Detection

  • Conference paper
  • First Online:
Third International Congress on Information and Communication Technology

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 797))

Abstract

Texture features quantitatively represent patterns of interest in image analysis and interpretation. Texture features can vary so largely that the analysis leads to interpretation errors and undesirable consequences. In such cases, finding of informative features becomes problematic. In medical imaging, the texture features were found useful for representing variations in patterns of pixel intensity, which were correlated with pathological changes. In this paper, we describe a new approach to extracting the texture features which are represented on the basis of Zernike orthogonal polynomials. We report the preliminary results which were obtained for a case of osteoarthritis detection in X-ray images using a deep learning paradigm known as group method of data handling.

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 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Bailey TC, Everson RM, Fieldsend JE, Krzanowski WJ, Partridge D, Schetinin V (2007) Representing classifier confidence in the safety critical domain: an illustration from mortality prediction in trauma cases. Neural Comput Appl 16(1):1–10. https://doi.org/10.1007/s00521-006-0053-y

    Article  Google Scholar 

  2. Boniatis I, Costaridou L, Cavouras D, Kalatzis I, Panagiotopoulos E, Panayiotakis G (2006) Osteoarthritis severity of the hip by computer-aided grading of radiographic images. Med Biol Eng Comput 44(9):793–803. https://doi.org/10.1007/s11517-006-0096-3

    Article  Google Scholar 

  3. Breiman L (2001) Random forests. Mach Learn 45(1):5–32. https://doi.org/10.1023/A:1010933404324

    Article  MATH  Google Scholar 

  4. Chatterjee S, Dey N, Shi F, Ashour AS, Fong SJ, Sen S (2017) Clinical application of modified bag-of-features coupled with hybrid neural-based classifier in dengue fever classification using gene expression data. Medical & biological engineering & computing. https://doi.org/10.1007/s11517-017-1722-y

    Article  Google Scholar 

  5. Depeursinge A, Al-Kadi O, Mitchell J (2017) Biomedical texture analysis: fundamentals, tools and challenges. Elsevier Science & Technology Books (2017)

    Google Scholar 

  6. Ghosh A, Sarkar A, Ashour AS, Balas-Timar D, Dey N, Balas VE (2015) Grid color moment features in glaucoma classification. Int J Adv Comput Sci Appl 6(9). https://doi.org/10.14569/IJACSA.2015.060913

  7. Haralick RM, Shanmugam K, Dinstein I (1973) Textural features for image classification. IEEE Trans Syst Man Cybern SMC-3(6):610–621. https://doi.org/10.1109/TSMC.1973.4309314

    Article  Google Scholar 

  8. Hastie T, Tibshirani R, Friedman J (2008) The elements of statistical learning. Springer, New York Inc., Springer Series in Statistics

    Google Scholar 

  9. Ivakhnenko A (1971) Polynomial theory of complex systems. IEEE Trans Syst Man Cybern SMC-1(4):364–378

    Article  MathSciNet  Google Scholar 

  10. Jakaite L, Schetinin V (2008) Feature selection for bayesian evaluation of trauma death risk. In: 14th Nordic-Baltic conference on biomedical engineering and medical physics: NBC 2008 Riga, Latvia. Springer Berlin Heidelberg, pp. 123–126. https://doi.org/10.1007/978-3-540-69367-3_33

    Google Scholar 

  11. Khotanzad A, Hong YH (1990) Invariant image recognition by zernike moments. IEEE Trans Pattern Anal Mach Intell 12(5):489–497. https://doi.org/10.1109/34.55109

    Article  Google Scholar 

  12. Krzanowski WJ, Bailey TC, Partridge D, Fieldsend JE, Everson RM, Schetinin V (2006) Confidence in classification: a bayesian approach. J Classif 23(2):199–220. https://doi.org/10.1007/s00357-006-0013-3

    Article  MathSciNet  MATH  Google Scholar 

  13. Li Z, Shi K, Dey N, Ashour AS, Wang D, Balas VE, McCauley P, Shi F (2017) Rule-based back propagation neural networks for various precision rough set presented kansei knowledge prediction: a case study on shoe product form features extraction. Neural Comput Appl 28(3):613–630. https://doi.org/10.1007/s00521-016-2707-8

    Article  Google Scholar 

  14. Maliavko AA, Gavrilov AV (2016) Towards development of self-learning and self-modification spiking neural network as model of brain. In: 2016 13th international scientific-technical conference on actual problems of electronics instrument engineering (APEIE), vol 2, pp 461–463. https://doi.org/10.1109/APEIE.2016.7806393

  15. Mller JA, Lemke F (2003) Self-organizing data mining: extracting knowledge from data. Trafford Publishing, Canada

    Google Scholar 

  16. Schetinin V, Fieldsend JE, Partridge D, Krzanowski WJ, Everson RM, Bailey TC, Hernandez A (2006) Comparison of the Bayesian and randomized decision tree ensembles within an uncertainty envelope technique. J Math Model Algorithms 5(4):397–416

    Article  MathSciNet  Google Scholar 

  17. Schetinin V, Jakaite L, Jakaitis J, Krzanowski W (2013) Bayesian decision trees for predicting survival of patients: a study on the US national trauma data bank. Comput Methods Programs Biomed 111(3):602–612. https://doi.org/10.1016/j.cmpb.2013.05.015

    Article  Google Scholar 

  18. Schetinin V, Schult J (2005) A neural-network technique to learn concepts from electroencephalograms. Theor Biosci 124(1):41–53. https://doi.org/10.1016/j.thbio.2005.05.004

    Article  Google Scholar 

  19. Schetinin V, Schult J (2006) Learning polynomial networks for classification of clinical electroencephalograms. Soft Comput 10(4):397–403. https://doi.org/10.1007/s00500-005-0499-3

    Article  Google Scholar 

  20. Schmidhuber J (2015) Deep learning in neural networks: an overview. Neural Netw 61:85–117

    Article  Google Scholar 

  21. Shamir L, Ling SM Jr, Scott WW, Bos A, Orlov N, Macura TJ, Eckley DM, Ferrucci L, Goldberg IG (2009) Knee X-ray image analysis method for automated detection of osteoarthritis. IEEE Trans Biomed Eng 56(2):407–415. https://doi.org/10.1109/TBME.2008.2006025

    Article  Google Scholar 

  22. Teague MR (1980) Image analysis via the general theory of moments. J Opt Soc Am 70(8):920–930. https://doi.org/10.1364/JOSA.70.000920

    Article  MathSciNet  Google Scholar 

  23. Uglov J, Jakaite L, Schetinin V, Maple C (2007) Comparing robustness of pairwise and multiclass neural-network systems for face recognition. EURASIP J Adv Signal Process 2008(1):468, 693. https://doi.org/10.1155/2008/468693

  24. Wang D, He T, Li Z, Cao L, Dey N, Ashour AS, Balas VE, McCauley P, Lin Y, Xu J, Shi F (2016) Image feature-based affective retrieval employing improved parameter and structure identification of adaptive neuro-fuzzy inference system. Neural computing and applications. https://doi.org/10.1007/s00521-016-2512-4

    Article  Google Scholar 

  25. Woloszynski T, Podsiadlo P, Stachowiak GW, Kurzynski M, Lohmander LS, Englund M (2012) Prediction of progression of radiographic knee osteoarthritis using tibial trabecular bone texture. Arthritis Rheum 64(3):688–695. https://doi.org/10.1002/art.33410

    Article  Google Scholar 

  26. Zemmal N, Azizi N, Dey N, Sellami M (2016) Adaptative s3vm semi supervised learning with features cooperation for breast cancer classification. J Med Imaging Health Inform 6(4):957–967

    Article  Google Scholar 

  27. Zharkova VV, Schetinin V (2003) A neural-network technique for recognition of filaments in solar images. In: In 7th international conference knowledge-based intelligent information and engineering systems KES 2003, Oxford. Springer, Berlin, Heidelberg, pp. 148–154. https://doi.org/10.1007/978-3-540-45224-9_22

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Computer Science and Technology, University of Bedfordshire, Luton, UK

    Mukti Akter & Livija Jakaite

Authors
  1. Mukti Akter
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Livija Jakaite
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Livija Jakaite .

Editor information

Editors and Affiliations

  1. School of Science and Technology, Middlesex University, London, UK

    Xin-She Yang

  2. University of Reading, Reading, UK

    Simon Sherratt

  3. Department of Information Technology, Techno India College of Technology, Kolkata, West Bengal, India

    Nilanjan Dey

  4. Sabar Institute of Technology, Sabarkantha, Gujarat, India

    Amit Joshi

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Akter, M., Jakaite, L. (2019). Extraction of Texture Features from X-Ray Images: Case of Osteoarthritis Detection. In: Yang, XS., Sherratt, S., Dey, N., Joshi, A. (eds) Third International Congress on Information and Communication Technology. Advances in Intelligent Systems and Computing, vol 797. Springer, Singapore. https://doi.org/10.1007/978-981-13-1165-9_13

Download citation

  • DOI: https://doi.org/10.1007/978-981-13-1165-9_13

  • Published:

  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-1164-2

  • Online ISBN: 978-981-13-1165-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation