Pattern Recognition of Inflammatory Sacroiliitis in Magnetic Resonance Imaging

  • Matheus Calil FaleirosEmail author
  • 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)


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.


Spondyloarthritis Inflammatory sacroiliitis Sacroiliac joints Magnetic resonance imaging Pattern recognition 


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Matheus Calil Faleiros
    • 1
    Email author
  • 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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