MCMC curve sampling for image segmentation.
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.
Laboratory for Information and Decision Systems, MIT, Cambridge, MA, USA. fan@mit.edu
Ayres C
AC
Fan
John W
JW
Fisher
William M
WM
Wells
James J
JJ
Levitt
Alan S
AS
Willsky
D000465:Algorithms; D001185:Artificial Intelligence; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008390:Markov Chains; D009010:Monte Carlo Method; D010363:Pattern Recognition, Automated / Q000379:methods*; D011467:Prostate / Q000033:anatomy & histology*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013788:Thalamus / Q000033:anatomy & histology*