Maximun a posteriori based coronary angiograms segmentation method with vessel-like feature and Markov Random Field

In this paper, we propose a Maximum a Posteriori (MAP) method which combined Markov Random Field (MRF) and the feature of vessel-like feature of the angiograms. As considering the advantages from both, the proposed method is sensitive to the vessel-like structures in the angiograms and robust to noise. Hence, it is able to extract the details of the vessels while at the same time reducing the noises. The proposed method is applied to clinic angiograms, the experiment results of the experiment on real angiograms concluded that this segmentation method is suitable for the segmentation of coronary angiography and is available for further accurate analyze to coronary angiography.