Resolution-appropriate shape representation

We present a new type of `snake' in which the dimensionality of the shapes is scaled appropriately for the resolution of the images in which the shapes are embedded. We define shapes as an ordered list of control points and compute the principal components of the shapes in a prior training set. Our energy function is based upon the Mahalanobis distance of a given shape from the mean shape and on the Mahalanobis distance of the image attributes from image attribute values extracted from the training set. We show that the derivative of this energy function with respect to the modal weights is reduced as the image resolution is reduced, and that the derivative of the energy scales with the variance associated with each mode. We exploit this property to determine the subset of the modes which are relevant at a particular level of image resolution, thereby reducing the dimensionality of the shapes. We implement a coarse-to-fine search procedure in the image and shape domains simultaneously, and demonstrate this procedure on the identification of anatomic structures in Computed Tomography images.