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.
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Faleiros, M.C., Ferreira Junior, J.R., Jens, E.Z., Dalto, V.F., Nogueira-Barbosa, M.H., de Azevedo-Marques, P.M. (2018). Pattern Recognition of Inflammatory Sacroiliitis in Magnetic Resonance Imaging. In: Tavares, J., Natal Jorge, R. (eds) VipIMAGE 2017. ECCOMAS 2017. Lecture Notes in Computational Vision and Biomechanics, vol 27. Springer, Cham. https://doi.org/10.1007/978-3-319-68195-5_69
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DOI: https://doi.org/10.1007/978-3-319-68195-5_69
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