A Support Vector Machine Based Algorithm for Magnetic Resonance Image Segmentation

In this work, we propose a kind of supervised classification - support vector machine (SVM) to segment magnetic resonance image (MRI). As a classifier, SVM can employ structural risk minimization principle and perform better in classification task. Based on those excellent capabilities of SVM, we conduct many detailed experiments on some standard simulated data and real data. According to the experiments results, SVM is proven to be a good classifier in MRI segmentation.

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