Skip to main content
SpringerLink
Log in

Early Detection of Osteoarthritis Using Local Binary Patterns: A Study Directed at Human Joint Imagery

  • Conference paper
  • First Online:
PRICAI 2016: Trends in Artificial Intelligence (PRICAI 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9810))

Included in the following conference series:

Abstract

Osteoarthritis (OA) is a chronic health condition that causes severe joint pain and stiffness; it is a major cause of disability in older people. The risk of OA increases from age 45 and older. Early diagnosis is typically made using X-ray imagery. In this paper an automated mechanism for OA screening is proposed. The fundamental idea is to generate a classifier that is able to distinguish between OA or non-OA images. The challenge is how bast to translate an X-ray image into a form that serves to both captures key information while remaining compatible with the classification process. It is suggested that image texture is the most desirable feature to be considered. The process is filly described and evaluated. The data used for the evaluation was obtained from the right Tibia of 50 female subjects. Excellent results were obtained, recorded AUC values of 1.0.

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 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
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

  • Ababneh, S., Gurcan, M.: An efficient graph-cut segmentation for knee bone osteoarthritis medical images. In: 2010 IEEE International Conference on Electro/Information Technology (EIT), pp. 1–4 (2011)

    Google Scholar 

  • Alattas, R., Barkana, B.: A comparative study of brain volume changes in alzheimer’s disease using MRI scans. In: 2015 IEEE Long Island, Systems, Applications and Technology Conference (LISAT), pp. 1–6 (2011)

    Google Scholar 

  • Arthritic Research UK.: Osteoarthritis in general practice – data and perspectives. Technical report, Arthritic Research UK, Copeman House, St Marys Gate, Chesterfield, S41 7TD (2013)

    Google Scholar 

  • Chen, K.-M., Chen, S.-Y.: Color texture segmentation using feature distributions. Pattern Recogn. Lett. 23(7), 755–771 (2002)

    Article  MATH  Google Scholar 

  • Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Pearson Prentice Hall, Upper Saddle River (2007)

    Google Scholar 

  • Hadid, A.: The local binary pattern approach and its applications to face analysis. In: Proceedings of the First Workshop on Image Processing Theory, Tools and Applications (IPTA), pp. 1–9. IEEE Computer Society (2008)

    Google Scholar 

  • Han, J., Ma, K.: Fuzzy color histogram and its use in color image retrieval. IEEE Trans. Image Process. 11(8), 944–952 (2002)

    Article  Google Scholar 

  • Heikkila, M., Pietikainen, M.: A texture-based method for modeling the background and detecting moving objects. IEEE Trans. Pattern Anal. Mach. Intell. 28(4), 657–662 (2006)

    Article  Google Scholar 

  • Heusch, G., Rodriguez, Y., Marcel, S.: Local binary patterns as an image preprocessing for face authentication. In: Proceedings of the Seventh IEEE International Conference on Automatic Face and Gesture Recognition (FGR), pp. 9–14. IEEE Computer Society (2006)

    Google Scholar 

  • Kachouie, N., Fieguth, P.A.: Medical texture local binary pattern for trus prostate segmentation. In: Engineering in Medicine and Biology Society, EMBS 2007, pp. 5605–5608 (2007)

    Google Scholar 

  • Nasir, M., Khanum, A., Baig, A.: Classification of brain tumor types in MRI scans using normalized cross-correlation in polynomial domain. In: 2014 12th International Conference on Frontiers of Information Technology (FIT), pp. 280–285 (2014)

    Google Scholar 

  • Ojala, T., Pietikainen, M., Maenpaa, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell. 24(7), 971–987 (2002)

    Article  MATH  Google Scholar 

  • Pietikäinen, M.: Image analysis with local binary patterns. In: Kalviainen, H., Parkkinen, J., Kaarna, A. (eds.) SCIA 2005. LNCS, vol. 3540, pp. 115–118. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  • Shamir, L., Ling, S., Scott, W., Bos, A., Orlov, N., Macura, T., Eckley, D., Ferrucci, L., Goldberg, I.: Knee x-ray image analysis method for automated detection of osteoarthritis. IEEE Trans. Biomed. Eng. 56(2), 407–415 (2009)

    Article  Google Scholar 

  • Soh, S.S., Swee, T.T., Ying, S.S., En, C.Z., Bin Mazenan, M., Meng, L.K.: Magnetic resonance image segmentation for knee osteoarthritis using active shape models. In: 2014 7th Biomedical Engineering International Conference (BMEiCON), pp. 1–5 (2014)

    Google Scholar 

  • Tanna, A.: Osteoarthritis “opportunities to address pharmaceutical gap”. Technical report, World Health Organisation Archive (2004)

    Google Scholar 

  • Udomchaiporn, A., Coenen, F., García-Fiñana, M., Sluming, V.: 3-D MRI brain scan classification using a point series based representation. In: Bellatreche, L., Mohania, M.K. (eds.) DaWaK 2014. LNCS, vol. 8646, pp. 300–307. Springer, Heidelberg (2014)

    Google Scholar 

  • Witten, I.H., Frank, E., Hall, M.A.: Data Mining: Practical Machine Learning Tools and Techniques, 3rd edn. Morgan Kaufmann, Amsterdam (2011)

    Google Scholar 

  • Wu, C., Lu, L., Li, Y.: A study on pattern encoding of local binary patterns for texture-based image segmentation. In: 2014 International Conference on Machine Learning and Cybernetics (ICMLC), vol. 2, pp. 592–596 (2014)

    Google Scholar 

  • Zahurul, S., Zahidul, S., Jidin, R.: An adept edge detection algorithm for human knee osteoarthritis images. In: International Conference on Signal Acquisition and Processing, ICSAP 2010, pp. 375–379 (2010)

    Google Scholar 

  • Zhao, G., Pietikainen, M.: Dynamic texture recognition using local binary patterns with an application to facial expressions. Proc. IEEE Int. Conf. Pattern Anal. Mach. Intell. (TPAMI) 29(6), 915–928 (2007)

    Article  Google Scholar 

Download references

Acknowledgments

We would like to thank Christian Schön from Braincon Handles GmbH, Vienna, for providing us with the image data used for evaluation purposes with respect to the work presented in this paper, and for his valuable comments regrading our results.

Author information

Authors and Affiliations

  1. Faculty of Technology and Environment, Prince of Songkla University, Phuket Campus, 80 Moo 1, Vichit-Songkram Road, Kathu, Phuket, Thailand

    Kwankamon Dittakan

  2. Department of Computer Science, University of Liverpool, Liverpool, L69 3BX, UK

    Frans Coenen

Authors
  1. Kwankamon Dittakan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Frans Coenen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kwankamon Dittakan .

Editor information

Editors and Affiliations

  1. Cardiff University, Cardiff, United Kingdom

    Richard Booth

  2. Southeast University , Nanjing, China

    Min-Ling Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Dittakan, K., Coenen, F. (2016). Early Detection of Osteoarthritis Using Local Binary Patterns: A Study Directed at Human Joint Imagery. In: Booth, R., Zhang, ML. (eds) PRICAI 2016: Trends in Artificial Intelligence. PRICAI 2016. Lecture Notes in Computer Science(), vol 9810. Springer, Cham. https://doi.org/10.1007/978-3-319-42911-3_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-42911-3_8

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-42910-6

  • Online ISBN: 978-3-319-42911-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation