This paper presents a new method for multiple structure segmentation, using a maximum a posteriori (MAP) estimation framework, based on prior shape densities involving nonparametric multivariate kernel density estimation of multiple shapes. Our method is motivated by the observation that neighboring or coupling structures in medical images generate configurations and co-dependencies which could potentially aid in segmentation if properly exploited. Our technique allows simultaneous segmentation of multiple objects, where highly contrasted, easy-to-segment structures can help improve the segmentation of weakly contrasted objects. We demonstrate the effectiveness of our method on both synthetic images and real magnetic resonance images (MRI) for segmentation of basal ganglia structures.
Nonparametric shape priors were utilized in REF for segmentation of multiple objects in images.
Coupled nonparametric shape priors for segmentation of multiple basal ganglia structures
2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro
2008 5th IEEE International Symposium on Biomedical Imaging: From Nano to Macro
Computer Science