A shape-based interpolation scheme for multidimensional images is presented. This scheme consists of first segmenting the given image data into a binary image, converting the binary image back into a gray image wherein the gray value of a point represents its shortest distance (positive value for points of the object and negative for those outside) from the cross-sectional boundary, and then interpolating the gray image. The set of all points with nonnegative values associated with them in the interpolated image constitutes the interpolated object. The method not only minimizes user involvement in interactive segmentation, but also leads to more accurate representation and depiction of dynamic as well as static objects. The general methodology and the implementation details of the method are presented and compared on a qualitative and quantitative basis to the existing methods. The generality of the proposed scheme is illustrated with a number of medical imaging examples.