Parameterized Feasible Boundaries in Gradient Vector Fields

摘要

Segmentation of (noisy) images containing a complex ensemble of objects is difficult to achieve on the basis of local image information only. It is advantageous to attack the problem of object boundary extraction by a model-based segmentation procedure. Segmentation is achieved by tuning the parameters of the geometrical model in such a way that the boundary template locates and describes the object in the image in an optimal way. The optimality of the solution is based on an objective function taking into account image information as well as the shape of the template. Objective functions in literature are mainly based on the gradient magnitude and a measure describing the smoothness of the template. In this contribution, we propose a new image objective function based on directional gradient information derived from Gaussian smoothed derivatives of the image data. The proposed method is designed to accurately locate an object boundary even in the case of a conflicting object positioned close to the object of interest. We further introduce a new smoothness objective to ensure the physical feasibility of the contour. The method is evaluated on artificial data. Results on real medical images show that the method is very effective in accurately locating object boundaries in very complex images.