Magnetic resonance image classification using nonnegative matrix factorization and ensemble tree learning techniques

This paper shows a magnetic resonance image (MRI) classification technique based on nonnegative matrix factorization (NNMF) and ensemble tree learning methods. The system consists of a feature extraction process that applies NNMF to gray matter (GM) MRI first-order statistics of a number of sub-cortical structures and a learning process of an ensemble of decision trees. The ensembles are trained by means of boosting and bagging while their performance is compared in terms of the classification error and the received operating characteristics curve (ROC) using k-fold cross validation. The results show that NNMF is well suited for reducing the dimensionality of the input data without a penalty on the performance of the ensembles. The best performance was obtained by bagging in terms of convergence rate and minimum residual loss, especially for high complexity classification tasks (i.e. NC vs. MCI and MCI vs. AD.