Modern Differential Geometry of Curves and Surfaces with Mathematica
Modern Differential Geometry of Curves and Surfaces with Mathematica
A Stochastic Iterative Closest Point Algorithm (stochastICP)
MICCAI '01 Proceedings of the 4th International Conference on Medical Image Computing and Computer-Assisted Intervention
Bone Segmentation and Fracture Detection in Ultrasound Using 3D Local Phase Features
MICCAI '08 Proceedings of the 11th international conference on Medical Image Computing and Computer-Assisted Intervention - Part I
Robust Point Set Registration Using Gaussian Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Self-calibrating ultrasound-to-CT bone registration
MICCAI'05 Proceedings of the 8th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
IEEE Transactions on Evolutionary Computation
IPCAI'12 Proceedings of the Third international conference on Information Processing in Computer-Assisted Interventions
MICCAI'12 Proceedings of the 15th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part II
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In order to use pre-operatively acquired computed tomography (CT) scans to guide surgical tool movements in orthopaedic surgery, the CT scan must first be registered to the patient's anatomy. Threedimensional (3D) ultrasound (US) could potentially be used for this purpose if the registration process could be made sufficiently automatic, fast and accurate, but existing methods have difficultiesmeeting one or more of these criteria.We propose a near-real-time US-to-CT registration method that matches point clouds extracted from local phase images with points selected in part on the basis of local curvature. The point clouds are represented as Gaussian Mixture Models (GMM) and registration is achieved by minimizing the statistical dissimilarity between the GMMs using an L2 distance metric. We present quantitative and qualitative results on both phantom and clinical pelvis data and show a mean registration time of 2.11 s with a mean accuracy of 0.49 mm.