Skip to main content
SpringerLink
Log in
Book cover

Topics in Medical Image Processing and Computational Vision pp 181–200Cite as

A Survey for the Automatic Classification of Bone Tissue Images

  • Chapter
  • First Online:

  • 1400 Accesses

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 8))

Abstract

In this chapter, a computer-assisted system aimed to assess the degree of regeneration of bone tissue from stem cells is built. We deal with phenotype and color analysis to describe a wide variety of microscopic biomedical images. Then we investigate several trained and non-parametric classifiers based on neural networks, decision trees, bayesian classifiers and association rules, whose effectiveness is analyzed to distinguish between bone and cartilage versus other existing types of tissue existing in our input biomedical images. The features selection includes texture, shape and color descriptors, among which we consider color histograms, Zernike moments and Fourier coefficients. Our study evaluates different selections for the feature vectors to compare accuracy and computational time as well as different stainings for revealing tissue properties. Overall, picrosirius reveals as the best staining and multilayer perceptron as the most effective classifier to distinguish between bone and cartilage tissue.

This is a preview of subscription content, 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   149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   199.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   199.99
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

Learn about institutional subscriptions

References

  1. Alegre Gutiérrez E, Sánchez González L, Alaiz Rodríguez R, Domínguez-Fernández Tejerina JC (2004) Utilización de Momentos Estadiísticos Y Redes Neuronales en la clasificación de Cabezas de Espermatozoides de Verraco (in spanish). XXV Jornadas de Automática

    Google Scholar 

  2. Andrades JA, Santamaría J, Nimni M, Becerra J (2001) Selection, amplification and induction of a bone marrow cell population to the chondro-osteogenic lineage by rhOP-1: an in vitro and in vivo study. Int J Dev Biol 45:683–693

    Google Scholar 

  3. Gil JE, Aranda JP, Mérida-Casermeiro E, Ujaldón M (2012) Efficient biomarkers for the characterization of bone tissue. Int J Numer Methods Biomed Eng (in press). doi:10.1002/cnm.2505

  4. Hall M, Frank E, Holmes G, Pfahringer B, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor 11(1):10–18

    Article  Google Scholar 

  5. Haykin S (1999) Neural networks: a comprehensive foundation. IEEE Press, New York, pp 135–155

    Google Scholar 

  6. Hubert M, Engelen S (2004) Robust PCA and classification in biosciences. Bioinformatics 20(11):1728–1736

    Article  Google Scholar 

  7. Hyvarinen A (1999) Survey on independent component analysis. Neural computing surveys, pp 94–128

    Google Scholar 

  8. Kohavi R (1995) A study of cross-validation and bootstrap for accuracy estimation and model selection. In: \(14\text{th}\) international conference on joint artificial intelligence vol 2, pp 1137–1143

    Google Scholar 

  9. Kroon DJ (2010) Quasi Newton limited memory BFGS and steepest

    Google Scholar 

  10. Li Y (1992) Reforming the theory of invariant moments for pattern recognition. Pattern Recog 25(7):723–730

    Article  Google Scholar 

  11. Martín-Requena MJ, Ujaldón M (2011) Leveraging graphics hardware for and automatic classification of bone tissue, Chap. 19. Computational methods in applied sciences. Computational vision and medical image processing—recent trends, pp 209–228

    Google Scholar 

  12. Piatetsky-Shapiro G (2007) Data mining and knowledge discovery 1996 to 2005: overcoming the hype and moving from university to business and analytics. Data Min Knowl Discov 15(1):99–105

    Article  MathSciNet  Google Scholar 

  13. Quinlan J (1988) Decision trees and multi-valued attributes. Mach Intell 11:305–319

    Google Scholar 

  14. Sharma G, Martín J (2009) Matlab: a language for parallel computing. Int J Parallel Program 37:3–36

    Article  MATH  Google Scholar 

  15. Tuceryan M, Jain AK (1998) Texture analysis. World Scientific Publishing, Singapore

    Google Scholar 

Download references

Acknowledgments

This work was supported by the Junta de Andalucía of Spain, under Project of Excellence P06-TIC-02109. We want to thank Silvia Claros, José Antonio Andrades and José Becerra from the Cell Biology Department at the University of Malaga for providing us the biomedical images used as input to our experimental analysis.

Author information

Authors and Affiliations

  1. Applied Mathematics Department, University of Malaga, Málaga, Spain

    J. E. Gil, J. P. Aranda & E. Mérida-Casermeiro

  2. Computer Architecture Department, University of Malaga, Málaga, Spain

    E. Mérida-Casermeiro & M. Ujaldón

Authors
  1. J. E. Gil
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. J. P. Aranda
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. E. Mérida-Casermeiro
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. Ujaldón
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to M. Ujaldón .

Editor information

Editors and Affiliations

  1. Faculdade de Engenharia, Departamento de Engenharia Mecânica, Universidade do Porto, Dr. Roberto Frias, Porto, 4200-465, Portugal

    João Manuel R.S. Tavares

  2. Faculdade de Engenharia, Departamento de Engenharia Mecânica, Universidade do Porto, Porto, Porto, 4200-465, Portugal

    Renato M. Natal Jorge

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer Science+Business Media Dordrecht

About this chapter

Cite this chapter

Gil, J.E., Aranda, J.P., Mérida-Casermeiro, E., Ujaldón, M. (2013). A Survey for the Automatic Classification of Bone Tissue Images. In: Tavares, J., Natal Jorge, R. (eds) Topics in Medical Image Processing and Computational Vision. Lecture Notes in Computational Vision and Biomechanics, vol 8. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-0726-9_10

Download citation

  • DOI: https://doi.org/10.1007/978-94-007-0726-9_10

  • Published:

  • Publisher Name: Springer, Dordrecht

  • Print ISBN: 978-94-007-0725-2

  • Online ISBN: 978-94-007-0726-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation