Chan Vese (CV) model

which has good ability to handle the blurry boundary and complex topological structures in images

has been widely used in image segmentations. However

the MR image which has intensity inhomogeneity cannot be segmented accurately by the CV model. And it needs computing all the data of the image during the iterative course. Arming at these disabilities

a fast method of CV model based on statistics in local regions is proposed. First

by calculating the statistics in local regions

Bayesian posterior probabilities that decide which class the pixels belong to are obtained

which are the foundations of the evolution of curve. In this way

the MR images can be segmented accurately. And then two lists are set for storing joint points inside and outside of the curve

and it only need to update the two lists to evolve the curve. By this method

it not only saves lots of time but also preserves the advantages of level set methods

such as the automatic handling of topological changes.