In this paper, we examine the use of implicit shape representations for nonrigid registration of serial CT liver examinations. Using ground truth in the form of corresponding landmarks manually labeled by a radiotherapist, we carry out an experiment to determine whether nonrigid registration performs better when applied to the original image data or to images constructed from implicit representations of the liver. We compare a variety of standard regularizers (elastic, diffusion, and curvature), similarity measures (sum of squared differences and mutual information), and weighting factors, using three different implicit shape representations: the Euclidean Distance Transform, the Poisson Transform (based on the expected hitting time of a random walk), and a new transform designed to highlight concavities in the shape.
['Nathan D. Cahill', 'G. Vesom', 'Lena Gorelick', 'J. Brady', 'J.A. Noble', 'J.M. Brady']
Investigating implicit shape representations for alignment of livers from serial CT examinations