A shape-based segmentation approach: an improved technique using level sets

We propose a novel approach for shape-based segmentation based on a specially designed level set function format. This format permits us to better control the process of object registration which is an important part in the shape-based segmentation framework. The method depends on a set of training shapes used to build a parametric shape model. The color is taken into consideration besides the shape prior information. The shape model is fitted to the image volume by registration through an energy minimization problem. The approach overcomes the conventional methods problems like point correspondences and weighing coefficients tuning of the partial differential equations (PDE's). Also it is suitable for multidimensional data and computationally efficient. Results of extracting the 2D star fish and the brain ventricles in 3D demonstrate the efficiency of the approach.