Registration of PET and MR hand volumes using bayesian networks

Authors:
Derek Magee;Steven Tanner;Michael Waller;Dennis McGonagle;Alan P. Jeavons
Affiliations:
School of Computing/Academic Unit of Medical Physics, University of Leeds, UK;School of Computing/Academic Unit of Medical Physics, University of Leeds, UK;Leeds Teaching Hospitals NHS Trust, Leeds, UK;Leeds Teaching Hospitals NHS Trust, Leeds, UK;School of Computing/Academic Unit of Medical Physics, University of Leeds, UK
Venue:
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Year:
2005

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Visual Tracking of High DOF Articulated Structures: an Application to Human Hand Tracking

ECCV '94 Proceedings of the Third European Conference-Volume II on Computer Vision - Volume II

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Abstract

A method for the non-rigid, multi-modal, registration of volumetric scans of human hands is presented. PET and MR scans are aligned by optimising the configuration of a tube based model using a set of Bayesian networks. Efficient optimisation is performed by posing the problem as a multi-scale, local, discrete (quantised) search, and using dynamic programming. The method is to be used within a project to study the use of high-resolution HIDAC PET imagery in investigating bone growth and erosion in arthritis.