Many applications of computer vision requires segmenting out of an object of interest from a given image. Motivated by unlevel-sets formulation of Raviv, Kiryati and Sochen  and statistical formulation of Leventon, Crimson and Faugeras , we present a new image segmentation method which accounts for prior shape information. Our method depends on Ambrosio-Tortorelli approximation of Mumford-Shah functional. The prior shape is represented by a by-product of this functional, a smooth edge indicator function, known as the "edge strength function", which provides a distance-like surface for the shape boundary. Our method can handle arbitrary deformations due to shape variability as well as plane Euclidean transformations. The method is also robust with respect to noise and missing parts. Furthermore, this formulation does not require simple closed curves as in a typical level set formulation.