Markov random field modeling in computer vision
Markov random field modeling in computer vision
An approach to 2D/3D registration of a vertebra in 2D X-ray fluoroscopies with 3D CT images
CVRMed-MRCAS '97 Proceedings of the First Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery
Image registration: convex weighting functions for histogram-based similarity measures
CVRMed-MRCAS '97 Proceedings of the First Joint Conference on Computer Vision, Virtual Reality and Robotics in Medicine and Medial Robotics and Computer-Assisted Surgery
Registration of Planar Film Radiographs with Computed Tomography
MMBIA '96 Proceedings of the 1996 Workshop on Mathematical Methods in Biomedical Image Analysis (MMBIA '96)
Fast Intensity-based 2D-3D Image Registration of Clinical Data Using Light Fields
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Computer Methods and Programs in Biomedicine
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention
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One of the main factors that affect the accuracy of intensity-based registration of two-dimensional (2D) X-ray fluoroscopy to three-dimensional (3D) CT data is the similarity measure, which is a criterion function that is used in the registration procedure for measuring the quality of image match. This paper presents a unifying framework for rationally deriving point similarity measures based on Markov random field (MRF) modeling of difference images which are obtained by comparing the reference fluoroscopic images with their associated digitally reconstructed radiographs (DRR's). The optimal solution is defined as the maximum a posterior (MAP) estimate of the MRF. Three novel point similarity measures derived from this framework are presented. They are evaluated using a phantom and a human cadaveric specimen. Combining any one of the newly proposed similarity measures with a previously introduced spline-based registration scheme, we develop a fast and accurate registration algorithm. We report their capture ranges, converging speeds, and registration accuracies.