MCMC curve sampling for image segmentation

Authors:
Ayres C. Fan;John W. Fisher, III.;William M. Wells, III.;James J. Levitt;Alan S. Willsky
Affiliations:
Laboratory for Information and Decision Systems, MIT, Cambridge, MA;Laboratory for Information and Decision Systems, MIT, Cambridge, MA and Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA;Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA and Brigham and Women's Hospital, Harvard Medical School, Boston, MA;Brigham and Women's Hospital, Harvard Medical School, Boston, MA and Dept. of Psychiatry, VA Boston HCS, Harvard Medical School, Brockton, MA;Laboratory for Information and Decision Systems, MIT, Cambridge, MA
Venue:
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
Year:
2007

Citing 5
Cited 2

Image Segmentation by Data-Driven Markov Chain Monte Carlo

IEEE Transactions on Pattern Analysis and Machine Intelligence
Stochastic Motion and the Level Set Method in Computer Vision: Stochastic Active Contours

International Journal of Computer Vision
Asymptotic global confidence regions in parametric shape estimation problems

IEEE Transactions on Information Theory
Active contours without edges

IEEE Transactions on Image Processing
Curve evolution implementation of the Mumford-Shah functional for image segmentation, denoising, interpolation, and magnification

IEEE Transactions on Image Processing

Detection of Deformable Objects in 3D Images Using Markov-Chain Monte Carlo and Spherical Harmonics

MICCAI '08 Proceedings of the 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Part II
Summarizing and visualizing uncertainty in non-rigid registration

MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

We present an algorithm to generate samples from probability distributions on the space of curves. We view a traditional curve evolution energy functional as a negative log probability distribution and sample from it using a Markov chain Monte Carlo (MCMC) algorithm. We define a proposal distribution by generating smooth perturbations to the normal of the curve and show how to compute the transition probabilities to ensure that the samples come from the posterior distribution. We demonstrate some advantages of sampling methods such as robustness to local minima, better characterization of multi-modal distributions, access to some measures of estimation error, and ability to easily incorporate constraints on the curve.