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

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 machine (SVM), which has the ability of learning the nonlinear distribution of the tumor data without using any prior knowledge. Extensive experimental results obtained from real patients’ medical images clearly show that the proposed unsupervised one-class SVM segmentation method outperforms supervised two-class SVM segmentation method in terms of segmentation accuracy, speed and with less human intervention.