Prostate segmentation in Magnetic Resonance (MR) Images is a significant yet challenging task for prostate cancer treatment. Most of the existing works attempted to design a global classifier for all MR images, which neglect the discrepancy of images across different patients. To this end, we propose a novel transfer approach for prostate segmentation in MR images. Firstly, an image-specific classifier is built for each training image. Secondly, a pair of dictionaries and a mapping matrix are jointly obtained by a novel Semi-Coupled Dictionary Transfer Learning (SCDTL). Finally, the classifiers on the source domain could be selectively transferred to the target domain (i.e. testing images) by the dictionaries and the mapping matrix. The evaluation demonstrates that our approach has a competitive performance compared with the state-of-the-art transfer learning methods. Moreover, the proposed transfer approach outperforms the conventional deep neural network based method.