Tumor Segmentation from Magnetic Resonance Imaging by Learning via One-class Support Vector Machine

@inproceedings{Zhang2003TumorSF,
  title={Tumor Segmentation from Magnetic Resonance Imaging by Learning via One-class Support Vector Machine},
  author={Jianguo Zhang and Kai-Kuang Ma and Meng Hwa Er},
  year={2003}
}
In image segmentation, one challenge is how to deal with the nonlinearity of real data distribution, which often makes segmentation methods need more human interactions and make unsatisfied segmentation results. In this paper, we formulate this research issue as a one-class learning problem from both theoretical and practical viewpoints with application on medical image segmentation. For that, a novel and user-friendly tumor segmentation method is proposed by exploring one-class support vector… CONTINUE READING
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