A Bayesian approach to dynamic contours through stochastic sampling and simulated annealing

In many applications of image analysis, simply connected objects are to
be located in noisy images. During the last 5-6 years active contour
models have become popular for finding the contours of such objects.
Connected to these models are iterative algorithms for finding the
minimizing energy curves making the curves behave dynamically through
the iterations. These approaches do however have several disadvantages.
The numerical algorithms that are in use constraint the models that can
be used. Furthermore, in many cases only local minima can be achieved.
In this paper, we discuss a method for curve detection based on a fully
Bayesian approach. A model for image contours which allows the number
of nodes on the contours to vary is introduced. Iterative algorithms
based on stochastic sampling is constructed, which make it possible to
simulate samples from the posterior distribution, making estimates and
uncertainty measures of specific quantities available. Further,
simulated annealing schemes making the curve move dynamically towards
the global minimum energy configuration are presented. In theory, no
restrictions on the models are made. In practice, however,
computational aspects must be taken into consideration when choosing
the models. Much more general models than the one used for active
contours may however be applied. The approach is applied to ultrasound
images of the left ventricle and to Magnetic Resonance images of the
human brain, and show promising results.