Gaussian and morphological scale space for shape analysis of medical images

The construction of scale space requires the smoothing of the given image to generate a set of corresponding images at other coarser scales, and the extraction of features at these scales. Various methods of smoothing, combined with various feature extractors, will result in drastically different scale space representations. The particular application and desired criteria determine the choice of smoothing and feature extractor. In this paper, we first put forward a framework for the scale space representation. Then we will focus on the Gaussian and morphological scale space for planar shape analysis of medical images. The discussion of the various scale space methods is organized into three categories - boundary approach, region approach, and hybrid approach. Properties, limitations, performance, and applications of these scale space methods are discussed.