A Method for Multi-Scale Representation of Data Sets Based on Maximum Gradient Profiles: Initial Results on Angiographic Images

Abstract

Our previous work on knowledge based interpretation of angiographic images (Ritchings and Colchester 1986; Ritchings et al 1986; Wang et al 1987) has underlined the need to establish a hierarchical data structure which represents the image at multiple scales and which allows simplified control of the interaction between image-driven and knowledge-driven processes. Multi-resolution methods which use Gaussian blurring of different degrees in the analysis of features at multiple scales (Pizer et al 1987, 1988) have some theoretical advantages but, in practice, the selection of the most appropriate scales at which to smooth the image is difficult. We have developed a hierarchical algorithm which groups small elements into larger elements and then groups these into larger elements and so on. The size of the groups is determined by local criteria and prior decisions about scale are not required.