segmentation of multiple brain structures using coupled nonparametric shape priors
This paper presents a new approach for segmentation of multiple brain structures. We introduce a new coupled shape prior for neighboring structures in magnetic resonance images (MRI) for multi object segmentation problem, where the information obtained from images can not provide enough contrast or exact boundary. In segmentation of low contrasted brain structures we take the advantage of using prior information enforced by interaction between neighboring structures in a nonparametric estimation fashion. Using nonparametric density estimation of multiple shapes, we introduce the coupled shape prior information into the segmentation process which is based on active contour models. We demonstrate the effectiveness of our method on real magnetic resonance images in challenging segmentation scenarios where existing methods fail.