A segmentation method using compound Markov random fields based on a general boundary model
Markov random field (MRF) theory has widely been applied to segmentation in noisy images. This paper proposes a new MRF method. First, it couples the original labeling MRF with a boundary MRF that can help improve the performance of segmentation. Second, the boundary model is general and does not need prior training. Third, unlike existing related work, our model offers more compact interaction between the two MRFs. Experiments on synthetic images and real clinical datasets show that the proposed approach is able to produce good segmentation results, especially removing noise in low signal-to-noise ratio regions.