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A Survey for the Automatic Classification of Bone Tissue Images

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

Keywords

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.

Notes

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.

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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

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