Computer Vision And Pattern Analysis Laboratory Home Page  Home
People  People
Publications  Publications
Publications  Databases
Contact Information  Contact
Supported Research Projects  Supported Research Projects
Research Activites  Research Activites
Research Groups
SPIS - Signal Processing and Information Systems Lab.SPIS - Signal Processing and Information Systems Lab.
Medical Vision and Analysis Group  Medical Research Activities
Biometrics Research Group  Biometrics Research Group
SPIS - Signal Processing and Information Systems Lab.MISAM - Machine Intelligence for Speech Audio and Multimedia.
Knowledge Base
  Paper Library
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.
  download full paper

Home Back