Segmentation with MCMC Curve Sampling
SSG, LIDS, MIT
Segmentation is the process of dividing an image into coherent regions.
We introduce a segmentation method based on Markov Chain Monte Carlo
(MCMC) sampling techniques. The advantages of sampling methods over
traditional optimization-based methods include better robustness to
local minima, natural handling of multi-modal distributions, and access
to higher-order statistics. With MCMC sampling methods, we wish to
sample from a distribution p. To do so, we iteratively sample from a
proposal distribution q and accept or reject those samples according to
a decision rule. If the decision rule satisfies detailed balance, then
the iterations will converge to samples from p. We detail some sampling
methods based on both continuous and discrete curve evolution
formulations. We show how our methods satisfy detailed balance and
demonstrate some initial results on the prostate.
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