Nonparametric intensity priors for level set segmentation of low contrast structures.

Segmentation of low contrast objects is an important task in clinical applications like lesion analysis and vascular wall remodeling analysis. Several solutions to low contrast segmentation that exploit high-level information have been previously proposed, such as shape priors and generative models. In this work, we incorporate a priori distributions of intensity and low-level image information into a nonparametric dissimilarity measure that defines a local indicator function for the likelihood of belonging to a foreground object. We then integrate the indicator function into a level set formulation for segmenting low contrast structures. We apply the technique to the clinical problem of positive remodeling of the vessel wall in cardiac CT angiography images. We present results on a dataset of twenty five patient scans, showing improvement over conventional gradient-based level sets.