Active contours without edges

In this paper, we propose a new model for active contours to detect objects in a given image, based on techniques of curve evolution, Mumford-Shah fun ctional for segmentation and level sets. Our model can detect objects whose boundaries are not necessarily defined by gradient. We minimize an energy which can he seen as a particular case of the minimal partition problem, In the level set formulation, the problem becomes a "mean-curvature flow"-lik e evolving the active contour, which will stop on the desired boundary. How ever, the stopping term does not depend on the gradient of the. image, as i n the classical active contour models, hut is instead related to a particul ar segmentation of the image. We will give a numerical algorithm using fini te differences. Finally, we will present various experimental results and i n particular some examples for which the classical snakes methods based on the gradient are not applicable. Also, the initial curve can be anywhere in the image, and interior contours are automatically detected.