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Classification of magnetic resonance images

Classification of magnetic resonance images,Katarina Trojacanec,Gjorgji Madzarov,Dejan Gjorgjevikj,Suzana Loskovska

Classification of magnetic resonance images   (Citations: 1)
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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.
Published in 2010.
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    • ...We organized them in a hierarchical manner (Fig. 1) [10]...
    • ...Table 1 depicts the distribution of the number of images through the classes [10]...
    • ...As a result of our previous work [10], we concluded that the Edge Histogram Descriptor (EHD) is the most appropriate descriptor taking into account the examined descriptors [10]...
    • ...As a result of our previous work [10], we concluded that the Edge Histogram Descriptor (EHD) is the most appropriate descriptor taking into account the examined descriptors [10]...

    Katarina Trojacanecet al. Hierarchical classification architectures applied to Magnetic Resonanc...

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