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This paper introduces a discriminative method for semiautomated segmentation of the tumorous tissues. Due to the large data of 3D MR brain images and the blurry boundary of the pathological tissues, the segmentation is difficult. A non-parametric Bayesian Gaussian process is proposed to be used for the semi-supervised mode. This discriminative method uses both labeled data and a subset of unlabeled data sampling from 2D/3D images to classify the remains, which is called inductive problem. We propose the prior of traditional Gaussian process to be based on graph regularization and develop a new conditional probability named Extended Bernoulli Model to realize the induction. Fast algorithm to speed up the training phase is also implemented. Experimental results show our approach produces satisfactory segmentations corresponding to the manually labeled results by experts.