Most variational active contour models are designed to find the “desirable” local minima of data-dependent energy functionals with the hope of avoiding undesirable configurations due to noise or complex image structure. As such, there has been much research into the design of complex region-based energy functionals that are less likely to yield undesirable local minima. Unfortunately, most of these more “robust” region-based energy functionals are applicable to a much narrower class of imagery due to stronger assumptions about the underlying image data. Devising new implementation algorithms for active contours that attempt to capture more global minimizers of already proposed image-based energies would allow us to choose an energy that makes sense for a particular class of energy without concern over its sensitivity to local minima. However, sometimes the completely-global minimum is just as undesirable as a minimum that is too local.
In this paper, we propose a novel, fast and flexible dual front implementation of active contours, motivated by minimal path techniques and utilizing fast sweeping algorithms, which is easily manipulated to yield minima with variable “degrees” of localness and globalness. The ability to gracefully move from capturing minima that are more local (according to the initial placement of the active contour/surface) to minima that are more global makes it much easier to obtain “desirable” minimizers (which often are neither the most local nor the most global). As the examples, we illustrate the 2D and 3D implementations of this dual-front active contour for image segmentation from MRI imagery.