Distribution-based Level Set Segmentation for Brain MR Images

In this paper, we propose a distribution-based active contour model for brain MRI segmentation. As a generalization of the Chan-Vese piecewise-constant model, our solution uses Bayesian a posterior probabilities as the driving forces for curve evolution. Distribution prior, if available, can be seamlessly integrated into the level set evolution procedure. Unlike other region-based active contour models, our solution relaxes the global piecewise-constant assumption, and uses locally varying Gaussians to better account for intensity inhomogeneity and local variations existing in many MR images. More accurate and robust segmentations are therefore achieved. Experiments conducted on synthetic and real brain MRIs demonstrate the improvement made by our model.