Profile Scale-spaces for Multiscale Image Match

Abstract. Anatomical objects often have complex and varying image appearance at different portions of the boundary; and it is frequently a challenge even to select appropriate scales at which to sample the image. This motivates Bayesian image-match models which are both multiscale and statistical. We present a novel image-match model for use in Bayesian segmentation, a multiscale extension of image profile models akin to those in Active Shape Models. A spherical-harmonic based 3D shape representation provides a mapping of the object boundary to the sphere S 2, and a scale-space for profiles on the sphere defines a scalespace on the object. A key feature is that profiles are not blurred across the object boundary, but only along the boundary. This profile scalespace is sampled in a coarse-to-fine fashion to produce features for the statistical image-match model. A framework for model-building and segmentation has been built, and testing and validation are in progress with a dataset of 70 segmented images of the caudate nucleus