Segmentation of brain magnetic resonance angiography images based on MAP-MRF with multi-pattern neighborhood system

Existing maximum a posteriori probability and Markov random field (MRF) models have limitations associated with that the ordinary neighborhood system being unable to differentiate subtle changes due to several-to-one correspondence within the neighborhood. Aiming at overcoming the limitations and applications to segmentation of cerebral vessels from magnetic resonance angiography images, we proposed a multi-pattern neighborhood system and corresponding energy equation to enable the MRF model for segmenting fine cerebral vessels with complicated context. In the implementation, a candidate space of cerebral vessels was employed to reduce the time-consumption, which was based on a threshold of the response to multi-scale filtering. A set of phantoms simulating segmentation challenges of vessels have been devised to quantitatively validate the algorithm. In addition, ten three-dimensional clinical datasets have been used to validate the algorithm qualitatively. It has been shown that the proposed method could yield smaller error and improve the spatial resolution of MRF model.