Advertisement

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

  • Graziano Aretusi
  • Lara Fontanella
  • Luigi Ippoliti
  • Arcangelo Merla
Chapter
  • 1.3k Downloads
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 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Barker, M., Rayens, W.: Partial Least Squares for Discrimination. Journal of Chemometrics 17, 166–173 (2003)CrossRefGoogle Scholar
  2. 2.
    Belch, J.: Raynaud’s phenomenon. Its relevance to scleroderma. Ann. Rheum. Dis. 50, 839–845 (2005)CrossRefGoogle Scholar
  3. 3.
    Besag, J.E., Moran, A.P.: On the estimation and testing of spatial interaction in Gaussian lattice processes. Biometrika 62, 555–562 (1975)zbMATHCrossRefMathSciNetGoogle Scholar
  4. 4.
    Block, J.A., Sequeira, W.: Raynaud’s phenomenon. Lancet 357, 2042–2048 (2001)CrossRefGoogle Scholar
  5. 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)CrossRefGoogle Scholar
  6. 6.
    Cressie, N.A.: Statistics for spatial data. second edn., Wiley and Sons, New York, (1993)Google Scholar
  7. 7.
    Cocquerez, J.P., Philipp, S.: Analyse d’images: filtrage et segmentation. Masson, Paris (1995)Google Scholar
  8. 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)CrossRefMathSciNetGoogle Scholar
  9. 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)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 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)CrossRefGoogle Scholar
  11. 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. 12.
    Johnson, R.A., Wichern, D.W.: Applied Multivariate Statistical Analysis. sixth edn., Pearson education, Prentice Hall, London (2007)zbMATHGoogle Scholar
  13. 13.
    Maldague, X.P.V.: Theory and practice of infrared technology for nondestructive Testing. Wiley Interscience, New York (2001)Google Scholar
  14. 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. 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. 16.
    Rue, H., Held, L.: Gaussian Markov random Fields. Theory and Applications. Chapman and Hall/CRC, Boca Raton (2005)zbMATHCrossRefGoogle Scholar
  17. 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. 18.
    Semmlow, J.L.: Biosignal and Biomedical Image Processing. CRC Press, Boca Raton (2004)CrossRefGoogle Scholar
  19. 19.
    Tibshirani, B.: Regression shrinkage and selection via the Lasso. Journal of the Royal Statistical Society, Series B, 58, 267–288 (1996)zbMATHMathSciNetGoogle Scholar
  20. 20.
    Zitova, B., Flusser, J.: Image registration methods: a survey. Image andVision Computing 21, 977–1000 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Italia 2010

Authors and Affiliations

  • Graziano Aretusi
    • 1
  • Lara Fontanella
    • 1
  • Luigi Ippoliti
    • 1
  • Arcangelo Merla
    • 2
  1. 1.Department of Quantitative Methods and Economic TheoryUniversity G. d’AnnunzioChieti-PescaraItaly
  2. 2.Clinical Sciences and Bioimaging Department Institute of Advanced Biomedical TechnologiesFoundation University G. d’AnnunzioChieti-PescaraItaly

Personalised recommendations