Active shape forcusing

摘要

This paper presents a framework for hierarchical shape description which enables quantitative and qualitative shape studies at multiple levels of image detail. It allows the capture of the global object shape at higher image scales, and to focus it down to finer details at decreasing levels of image scale. A multi-scale active contour model, whose energy function is regularized with respect to underlying geometric image structure in a natural scale setting, is developed for the purpose of implicit shape extraction or regularization with respect to scale. The resulting set of shapes is formulated and visualized as a multi-scale shape stack for the investigation of shape changes across scales. We demonstrate the functionality of this framework by applying it to a set of true fractal structures, and to 3D brain MRI. The framework is shown to be capable of recovering the fractal dimension of the fractal shapes directly from their embedding image context. The equivalent measure on the medical images and its potential for medical shape analysis is discussed.