Interpolation of data on the surface of a sphere
ACM Transactions on Mathematical Software (TOMS)
Block Matching: A General Framework to Improve Robustness of Rigid Registration of Medical Images
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Deformable M-Reps for 3D Medical Image Segmentation
International Journal of Computer Vision - Special Issue on Research at the University of North Carolina Medical Image Display Analysis Group (MIDAG)
Multiscale Medial Loci and Their Properties
International Journal of Computer Vision - Special Issue on Research at the University of North Carolina Medical Image Display Analysis Group (MIDAG)
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Liver transplantation affords a unique opportunity to assess and improve radiological imaging of the liver, as the full explanted liver is available for review and comparison. Quantitative comparison between the explanted liver and in vivo images acquired prior to transplantation requires accurate registration of images of the explanted liver to the radiological images. However, this registration problem is challenging because the orientation change and the deformation magnitude between the two image sets exceed the level assumed for most registration algorithms. This paper suggests a two-step registration process to overcome the difficulty: to first align the orientation of 3D liver models built from two sets of image data using maximum volume overlap as their similarity measurement, and second to deform one model to match the other. The key contribution of this paper is that it utilizes the global volumetric information and the asymmetry property of the liver model to determinately provide a simple and reliable initial point for further deformable model based registration. Our experimental data demonstrate the effectiveness of this approach.