Area and length minimizing flows for shape segmentation

A number of active contour models have been proposed that unify the curve evolution framework with classical energy minimization techniques for segmentation, such as snakes, The essential idea is to evolve a curve (in two dimensions) or a surface (in three dimensions) under constraints from image forces so that it clings to features of interest in an intensity image, Recently, the evolution equation has been derived from first principles as the gradient dow that minimizes a modified length functional, tailored to features such as edges, However, because the how may be slow to converge in practice, a constant (hyperbolic) term is added to keep the curve/surface moving in the desired direction, In this paper, we derive a modification of this term based on the gradient how derived from a weighted area functional, with image dependent weighting factor, When combined with the earlier modified Length gradient dow, we obtain a partial differential equation (PDE) that offers a number of advantages, as illustrated by several examples of shape segmentation on medical images. In many cases the weighted area how may be used on its own, with significant computational savings.