A Decision Support System for the Prediction of the Trabecular Fracture Zone

  • Vasileios Korfiatis
  • Simone Tassani
  • George K. Matsopoulos
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7477)


Prediction of trabecular fracture zone is a very important element for assessing the fracture risk in patients. The assumption that failure always occurs in local bands, the so called ‘fracture zones’, with the remaining regions of the structure largely unaffected has been visually verified. Researchers agreed that the identification of the weakest link in the trabecular framework can lead to the prediction of the fracture zone and consequently of the failure event. In this paper, a decision support system (DSS) is proposed for the automatic identification of fracture zone. Initially, an automatic methodological approach based on image processing is applied for the automatic identification of trabecular bone fracture zone in micro-CT datasets, after mechanical testing. Then, a local analysis of the whole specimen is performed on order to compare the structure (Volumes of Interest -VOI) of the broken region to the unbroken one. As a result, for every VOI, 29 morphometrical parameters were computed and used as initial features to the proposed DSS. The DSS comprises of two main modules: the feature selection module and the classifier. The feature selection module is used for reducing the initial size of the input features’ subset (29 features) and for keeping the most informative features in order to increase the classification’s module performance. To this end, the Sequential Floating Forward Selection (SFFS) algorithm with Fuzzy C-Means evaluation criterion was implemented. For the classification, several classification algorithms including the Multilayer Perceptron (MLP), the Support Vector Machines (SVM), the Naïve Bayesian (NB), the k-Nearest Neighbor (KNN) and the k-Means (KM) have been used. Comparing the performance of these classification algorithms, the SFFS-SVM scheme provided the best performance scoring 98% in terms of overall classification accuracy.


Trabecular Fracture Zone Decision Support Systems Machine Learning Feature Selection Sequential Floating Forward Selection (SFFS) Multilayer Perceptron (MLP) Support Vector Machines (SVM) Naïve Bayesian (NB) k-Nearest Neighbor (KNN) k-Means (KM) 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Vasileios Korfiatis
    • 1
  • Simone Tassani
    • 1
  • George K. Matsopoulos
    • 1
  1. 1.Institute of Communication and Computer SystemZografouGreece

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