Iterative Multigrid Regularization Techniques for Image Matching
SIAM Journal on Scientific Computing
Automatic Construction of 3D Statistical Deformation Models Using Non-rigid Registration
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Backward Euler discretization of fully nonlinear parabolic problems
Mathematics of Computation
Coupled PDEs for Non-Rigid Registration and Segmentation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
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In this paper we propose a novel similarity metric and a method for deformable registration of two images for a specific clinical application. The basic assumption in almost all deformable registration approaches is that there exist explicit correspondences between pixels across the two images. This principle is used to design image (dis)similarity metrics, such as sum of squared differences (SSD) or mutual information (MI). This assumption is strongly violated, for instance, within specific regions of images from abdominal or pelvic section of a patient taken at two different time points. Nevertheless, in some clinical applications, it is required to compute a smooth deformation field for all the regions within the image including the boundaries of such regions. In this paper, we propose a deformable registration method, which utilizes a priori intensity distributions of the regions delineated on one of the images to devise a new similarity measure that varies across regions of the image to establish a smooth and robust deformation field. We present validation results of the proposed method in mapping bladder, prostate, and rectum contours of computer tomography (CT) volumes of 10 patients taken for prostate cancer radiotherapy treatment planning and verification.