Hierarchical Classification of Magnetic Resonance Images

The objective of the paper is to explore classification on magnetic resonance images (MRI). In our work on MRI classification, two types of classification (flat and hierarchical) are addressed and explored. The examination is conducted on the dataset of magnetic resonance images that have hierarchical organization. All images are described by using Edge histogram descriptor for the feature extraction process. We compared the experimental results obtained from the hierarchical classification to the results provided by flat classification using different classifiers, such as SVM methods, k nearest neighbors, C4.5 algorithm and artificial neural networks. As a result, we concluded that the hierarchical classification technique outperforms all other explored classifiers for the examined dataset of magnetic resonance images.