Step-edge models as they have been used to model local intensity variation, only rarely are justified for the real case of image data. Due to finite apertures, the nature of scene geometry as well as discretization of the image, local intensity variations result in smooth transitions of varying width and local contrast. In order to appropriately deal with the robust detection and localization of image contrast, the authors propose the parametrized ramp transition as local signal model. The scale-space processing scheme for token extraction consists of a cascade of first band-pass filtering the raw data and a subsequent correlation of the result with a scaled first order derivative operator. The robust contrast detection within scale space and the estimation of local signal attributes in closed form is documented. The scheme can be extended to deal with intensity variations of different specificity.>

Differently scaled intensity discontinuities along the gradient direction of scaled oriented contrast edges define significant X-ray CT and MR image structures. Reliable detection and quantitative description of such scaled intensity discontinuities is achieved with a scale-space approach. It results in an explicit token representation which can be understood as a compact symbolic description of significant image structure, namely a set of quantitative attributes per contrast edge. Moreover, a multi-resolution approach is used to reconstruct the image intensity function from the token representation alone using a membrane as prior image model. Current results from the novel entire processing cascade, which combines a quantitative discontinuity description and a membrane-based intensity reconstruction, are presented.>