A Survey for the Automatic Classification of Bone Tissue Images

  • J. E. Gil
  • J. P. Aranda
  • E. Mérida-Casermeiro
  • M. Ujaldón
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 8)


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.


Principal Component Analysis Radial Basis Function Input Image Association Rule Alcian Blue 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



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.


  1. 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 AutomáticaGoogle Scholar
  2. 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 45:683–693Google Scholar
  3. 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. 4.
    Hall M, Frank E, Holmes G, Pfahringer B, Witten IH (2009) The WEKA data mining software: an update. SIGKDD Explor 11(1):10–18CrossRefGoogle Scholar
  5. 5.
    Haykin S (1999) Neural networks: a comprehensive foundation. IEEE Press, New York, pp 135–155Google Scholar
  6. 6.
    Hubert M, Engelen S (2004) Robust PCA and classification in biosciences. Bioinformatics 20(11):1728–1736CrossRefGoogle Scholar
  7. 7.
    Hyvarinen A (1999) Survey on independent component analysis. Neural computing surveys, pp 94–128Google Scholar
  8. 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 1137–1143Google Scholar
  9. 9.
    Kroon DJ (2010) Quasi Newton limited memory BFGS and steepestGoogle Scholar
  10. 10.
    Li Y (1992) Reforming the theory of invariant moments for pattern recognition. Pattern Recog 25(7):723–730CrossRefGoogle Scholar
  11. 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 209–228Google Scholar
  12. 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):99–105MathSciNetCrossRefGoogle Scholar
  13. 13.
    Quinlan J (1988) Decision trees and multi-valued attributes. Mach Intell 11:305–319Google Scholar
  14. 14.
    Sharma G, Martín J (2009) Matlab: a language for parallel computing. Int J Parallel Program 37:3–36zbMATHCrossRefGoogle Scholar
  15. 15.
    Tuceryan M, Jain AK (1998) Texture analysis. World Scientific Publishing, SingaporeGoogle Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • J. E. Gil
    • 1
  • J. P. Aranda
    • 1
  • E. Mérida-Casermeiro
    • 1
    • 2
  • M. Ujaldón
    • 2
  1. 1.Applied Mathematics DepartmentUniversity of MalagaMálagaSpain
  2. 2.Computer Architecture DepartmentUniversity of MalagaMálagaSpain

Personalised recommendations