Classification of Magnetic Resonance Images

Katarina Trojacanec, Gjorgji Madzarov, Dejan Gjorgjevikj, Suzana Loskovska

Abstract: The aim of the paper is to compare classification error of the classifiers applied to magnetic resonance images for each descriptor used for feature extraction. We compared several Support Vector Machine (SVM) techniques, neural networks and k nearest neighbor classifier for classification of Magnetic Resonance Images (MRIs). Different descriptors are applied to provide feature extraction from the images. The dataset used for classification contains magnetic resonance images classified in 9 classes.


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