A decision support system for the prediction of the trabecular fracture zone

Authors:
Vasileios Korfiatis;Simone Tassani;George K. Matsopoulos
Affiliations:
Institute of Communication and Computer System, Zografou, Athens, Greece;Institute of Communication and Computer System, Zografou, Athens, Greece;Institute of Communication and Computer System, Zografou, Athens, Greece
Venue:
ANNPR'12 Proceedings of the 5th INNS IAPR TC 3 GIRPR conference on Artificial Neural Networks in Pattern Recognition
Year:
2012

Citing 9
Cited 0

Floating search methods in feature selection

Pattern Recognition Letters
Support-Vector Networks

Machine Learning
On Weighting Clustering

IEEE Transactions on Pattern Analysis and Machine Intelligence
Editorial: Introduction to the special issue on decision support in medicine

Decision Support Systems
CT-MRI automatic surface-based registration schemes combining global and local optimization techniques

Technology and Health Care
Assessment and Classification of Mechanical Strength Components of Human Femur Trabecular Bone Using Texture Analysis and Neural Network

Journal of Medical Systems
Pattern Recognition, Fourth Edition

Pattern Recognition, Fourth Edition
Computational intelligence in gait research: a perspective on current applications and future challenges

IEEE Transactions on Information Technology in Biomedicine - Special section on computational intelligence in medical systems
Least squares quantization in PCM

IEEE Transactions on Information Theory

Quantified Score

Hi-index	0.00

Visualization

Abstract

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.