Skip to main content
Book cover

Complex Data Modeling and Computationally Intensive Statistical Methods pp 1–12Cite as

Space-time texture analysis in thermal infrared imaging for classification of Raynaud’s Phenomenon

  • Chapter

Part of the Contributions to Statistics book series (CONTRIB.STAT.)

Abstract

This paper proposes a supervised classification approach for the differential diagnosis of Raynaud’s Phenomenon on the basis of functional infrared imaging (IR) data. The segmentation and registration of IR images are briefly discussed and two texture analysis techniques are introduced in a spatio-temporal framework to deal with the feature extraction problem. The classification of data from healthy subjects and from patients suffering from primary and secondary Raynaud’s Phenomenon is performed by using Stepwise Linear Discriminant Analysis (LDA) on a large number of features extracted from the images. The results of the proposed methodology are shown and discussed for a temporal sequence of images related to 44 subjects.

Key words

  • Raynaud’s Phenomenon
  • classification
  • functional infrared imaging
  • texture analysis
  • Gaussian Markov Random Fields

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-88-470-1386-5_1
  • Chapter length: 12 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-88-470-1386-5
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Hardcover Book
USD   109.99
Price excludes VAT (USA)
Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Barker, M., Rayens, W.: Partial Least Squares for Discrimination. Journal of Chemometrics 17, 166–173 (2003)

    CrossRef  Google Scholar 

  2. Belch, J.: Raynaud’s phenomenon. Its relevance to scleroderma. Ann. Rheum. Dis. 50, 839–845 (2005)

    CrossRef  Google Scholar 

  3. Besag, J.E., Moran, A.P.: On the estimation and testing of spatial interaction in Gaussian lattice processes. Biometrika 62, 555–562 (1975)

    MATH  CrossRef  MathSciNet  Google Scholar 

  4. Block, J.A., Sequeira, W.: Raynaud’s phenomenon. Lancet 357, 2042–2048 (2001)

    CrossRef  Google Scholar 

  5. Chang, J.S., Liao, H.Y.M., Hor, M.K., Hsieh, J.W., Cgern, M.Y.: New automatic multilevel thresholding technique for segmentation of thermal images. Images and vision computing 15, 23–34 (1997)

    CrossRef  Google Scholar 

  6. Cressie, N.A.: Statistics for spatial data. second edn., Wiley and Sons, New York, (1993)

    Google Scholar 

  7. Cocquerez, J.P., Philipp, S.: Analyse d’images: filtrage et segmentation. Masson, Paris (1995)

    Google Scholar 

  8. Dryden, I.L., Ippoliti, L., Romagnoli, L.: Adjusted maximum likelihood and pseudo-likelihood estimation for noisy Gaussian Markov randomfields. Journal of Computational and Graphical Statistics 11, 370–388 (2002)

    CrossRef  MathSciNet  Google Scholar 

  9. Fontanella, L., Ippoliti, L., Martin, R.J., Trivisonno, S.: Interpolation of Spatial and Spatio-Temporal Gaussian Fields using Gaussian Markov Random Fields. Advances in Data Analysis and Classification 2, 63–79 (2008)

    MATH  CrossRef  MathSciNet  Google Scholar 

  10. Heriansyak, R., Abu-Bakar, S.A.R.: Defect detection in thermal image for nondestructive evaluation of petrochemical equipments. NDT&E International 42, 729–740 (2009)

    CrossRef  Google Scholar 

  11. Jarc, A., Pers, J., Rogelj, P., Perse, M., Kovacic, S.: Texture features for affine registration of thermal and visible images. Computer Vision Winter Workshop (2007)

    Google Scholar 

  12. Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis. sixth edn., Pearson education, Prentice Hall, London (2007)

    MATH  Google Scholar 

  13. Maldague, X.P.V.: Theory and practice of infrared technology for nondestructive Testing. Wiley Interscience, New York (2001)

    Google Scholar 

  14. Merla, A., Romani, G.L., Di Luzio, S., Di Donato, L., Farina, G., Proietti, M., Pisarri, S., Salsano, S.: Raynaud’s phenomenon: infrared functional imaging applied to diagnosis and drug effect. Int. J. Immunopathol. Pharmacol. 15(1), 41–52 (2002a)

    Google Scholar 

  15. Merla, A., Di Donato, L., Pisarri, S., Proietti, M., Salsano, F., Romani, G.L.: Infrared Functional Imaging Applied to Raynaud’s Phenomenon. IEEE Eng. Med. Biol. Mag. 6(73), 41–52 (2002b)

    Google Scholar 

  16. Rue, H., Held, L.: Gaussian Markov random Fields. Theory and Applications. Chapman and Hall/CRC, Boca Raton (2005)

    MATH  CrossRef  Google Scholar 

  17. Scribner, D.A., Schuller, J.M., Warren, P., Howard, J.G., Kruer, M.R.: Image preprocessing for the infrared. Proceedings of SPIE, the International Society for Optical Engineering 4028, 222–233 (2000)

    Google Scholar 

  18. Semmlow, J.L.: Biosignal and Biomedical Image Processing. CRC Press, Boca Raton (2004)

    CrossRef  Google Scholar 

  19. Tibshirani, B.: Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society, Series B, 58, 267–288 (1996)

    MATH  MathSciNet  Google Scholar 

  20. Zitova, B., Flusser, J.: Image registration methods: a survey. Image andVision Computing 21, 977–1000 (2003)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Quantitative Methods and Economic Theory, University G. d’Annunzio, Chieti-Pescara, Italy

    Graziano Aretusi, Lara Fontanella & Luigi Ippoliti

  2. Clinical Sciences and Bioimaging Department Institute of Advanced Biomedical Technologies, Foundation University G. d’Annunzio, Chieti-Pescara, Italy

    Arcangelo Merla

Authors
  1. Graziano Aretusi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lara Fontanella
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Luigi Ippoliti
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Arcangelo Merla
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Ca’ Foscari University of Venice, Venice, Italy

    Pietro Mantovan

  2. Politecnico di Milano, Milan, Italy

    Piercesare Secchi

Rights and permissions

Reprints and Permissions

Copyright information

© 2010 Springer-Verlag Italia

About this chapter

Cite this chapter

Aretusi, G., Fontanella, L., Ippoliti, L., Merla, A. (2010). Space-time texture analysis in thermal infrared imaging for classification of Raynaud’s Phenomenon. In: Mantovan, P., Secchi, P. (eds) Complex Data Modeling and Computationally Intensive Statistical Methods. Contributions to Statistics. Springer, Milano. https://doi.org/10.1007/978-88-470-1386-5_1

Download citation