Level set methods are effective for image segmentation problems. However, the methods suffer from limitations such as slow convergence and leaking problems. As such, over the past two decades, the original level set method has been evolved in many directions, including integration of prior shape models into the segmentation framework. In this paper, we introduce a new prior shape model for level set segmentation. With a shape model represented implicitly by a signed distance function, we incorporate a local shape parameter to the shape model. This parameter helps to regulate the model fitting process. Based on this local parameter of the shape model, we define a shape energy to drive the level set evolution for image segmentation. The shape energy is coupled with a Gaussian kernel, which acts as a weight distribution on the shape model. This Gaussian effect not only allows evolution of level set to deform around the shape model, but also provides a smoothing effect along the edges. Our approach presents a new dimension to extract local shape parameter directly from the shape model, which is different from previous work that focused on an indirect manner of feature extractions. Experimental results on synthetic, optical and MR images demonstrate the feasibility of this new shape model and shape energy.