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Pattern Recognition of Inflammatory Sacroiliitis in Magnetic Resonance Imaging

  • Matheus Calil Faleiros
  • José Raniery Ferreira Junior
  • Eddy Zavala Jens
  • Vitor Faeda Dalto
  • Marcello Henrique Nogueira-Barbosa
  • Paulo Mazzoncini de Azevedo-Marques
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 27)

Abstract

The standard reference to evaluate active inflammation of sacroiliac joints in spondyloarthritis is magnetic resonance imaging (MRI). However, visual evaluation may be challenging to specialists due to clinical variability. In order to improve the diagnosis of inflammatory sacroiliitis we have used image processing and machine learning technics to recognize inflammatory patterns in sacroiliac joints in spectral attenuated inversion recovery (SPAIR) T2-weighted MRI using gray-level, texture and spectral features. Pattern recognition was performed by the ReliefF method for attribute selection and the classifiers K nearest neighbors (with 5 values for K), Multilayer Perceptron artificial neural network, Naive Bayes, Random Forest, and Decision Tree J48. Classification was assessed by the area under the ROC (receiver operating characteristic) curve (AUC), Sensitivity and Specificity, with a 10-fold cross validation. The K nearest neighbors with K = 5 obtained the best performance with AUC up to 0.96.

Keywords

Spondyloarthritis Inflammatory sacroiliitis Sacroiliac joints Magnetic resonance imaging Pattern recognition 

References

  1. 1.
    Dalto, V.F., Assad, R.L., Crema, M.D., Louzada-Junior, P., Nogueira-Barbosa, M.H.: MRI assessment of bone marrow oedema in the sacroiliac joints of patients with spondyloarthritis: is the SPAIR T2w technique comparable to STIR? Eur. Radiol. (2017). doi: 10.1007/s00330-017-4746-7
  2. 2.
    Faleiros, M.C., Ferreira Junior, J.R., Dalto, V.F., Nogueira-Barbosa, M.H., Azevedo-Marques, P.M.: Avaliação computadorizada de sacroiliíte em imagens de ressonância magnética. In: XV Brazilian Congress of Health Informatics, pp. 85–94 (2016)Google Scholar
  3. 3.
    Frank, E., Hall, M., Witten, I.: The WEKA Workbench. Online Appendix for Data Mining: Practical Machine Learning Tools and Techniques, 4th edn. Morgan Kaufmann (2016)Google Scholar
  4. 4.
    Gonzalez, R., Woods, S.: Digital image processing. Addison-Wesley (1993)Google Scholar
  5. 5.
    Haralick, R., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. 3(6), 610–621 (1973)CrossRefGoogle Scholar
  6. 6.
    JFeatureLib open source project. https://github.com/locked-fg/JFeatureLib Sources: Haralick.java - Author: graf; Tamura.java - Author: Marko Keuschnig & Christian Penz; Histogram.java - Autor: graf. Accessed 15 Mar 2017
  7. 7.
    Kononenko, I.: Estimating attributes: analysis and extensions of RELIEF. In: European Conference on Machine Learning, pp. 171–182 (1994)Google Scholar
  8. 8.
    Maksymowych, W.P., Inman, R.D., Salonen, D., Dhillon, S.S., Williams, M., Stone, M., Conner-spady, B., Palsat, J., Lambert, R.G.: Spondyloarthritis research Consortium of Canada magnetic resonance imaging index for assessment of sacroiliac joint inflammation in ankylosing spondylitis. Arthritis Care Res. 53(5), 703–709 (2005)CrossRefGoogle Scholar
  9. 9.
    Pialat, J., Di Marco, L., Feydy, A., Peyron, C., Porta, B., Himpens, P., Ltaief-Boudrigua, A., Aubry, S.: Sacroiliac joints imaging in axial spondyloarthritis. Diagn. Interv. Imaging 97(7), 697–708 (2016)CrossRefGoogle Scholar
  10. 10.
    Rudwaleit, M., Jurik, A.G., Hermann, K.A., Landewé, R., van der Heijde, D., Baraliakos, X., Marzo-Ortega, H., Østergaard, M., Braun, J., Sieper, J.: Defining active sacroiliitis on magnetic resonance imaging (MRI) for classification of axial spondyloarthritis: a consensual approach by the ASAS/OMERACT MRI group. Ann. Rheum. Dis. 68(10), 1520–1527 (2009)CrossRefGoogle Scholar
  11. 11.
    Sampaio-Barros, P.D.: Epidemiology of spondyloarthritis in Brazil. Am. J. Med. Sci. 341(4), 287–288 (2011)CrossRefGoogle Scholar
  12. 12.
    Schneider, C.A., Rasband, W.S., Eliceiri, K.W.: NIH Image to ImageJ: 25 years of image analysis. Nat. Methods 9(7), 671 (2012)CrossRefGoogle Scholar
  13. 13.
    Stolwijk, C., van Onna, M., Boonen, A., van Tubergen, A.: Global prevalence of spondyloarthritis: a systematic review and meta-regression analysis. Arthritis Care Res. 68(9), 1320–1331 (2016)CrossRefGoogle Scholar
  14. 14.
    Tamura, H., Mori, S., Yamawaki, T.: Textural features corresponding to visual perception. IEEE Trans. Syst. Man Cybern. 8(6), 460–473 (1978)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Matheus Calil Faleiros
    • 1
  • José Raniery Ferreira Junior
    • 1
  • Eddy Zavala Jens
    • 1
  • Vitor Faeda Dalto
    • 1
  • Marcello Henrique Nogueira-Barbosa
    • 1
  • Paulo Mazzoncini de Azevedo-Marques
    • 1
  1. 1.Ribeirão Preto Medical SchoolUniversity of São PauloMonte Alegre, Ribeirão PretoBrazil

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