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Coupled Nonparametric Shape Priors for Segmentation of Multiple Basal Ganglia Structures
Authors: Gokhan Uzunbas, Mujdat Cetin, Gozde Unal, Aytul Ercil
Published in: ISBI 2008 - 2008 5th IEEE International Symposium on Biomedical Imaging
Publication year: 2008
Abstract: This paper presents a new method for multiple structure segmenta- tion, 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 mo- tivated 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 ob- jects, where highly contrasted, easy-to-segment structures can help improve the segmentation of weakly contrasted objects. We demon- strate the effectiveness of our method on both synthetic images and real magnetic resonance images (MRI) for segmentation of basal ganglia structures.
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