Sciweavers

Share
IPMI
2009
Springer

Bayesian Registration via Local Image Regions: Information, Selection and Marginalization

13 years 2 months ago
Bayesian Registration via Local Image Regions: Information, Selection and Marginalization
We propose a novel Bayesian registration formulation in which image location is represented as a latent random variable. Location is marginalized to determine the maximum a priori (MAP) transform between images, which results in registration that is more robust than the alternatives of omitting locality (i.e. global registration) or jointly maximizing locality and transform (i.e. iconic registration). A mathematical link is established between the Bayesian registration formulation and the mutual information (MI) similarity measure. This leads to a novel technique for selecting informative image regions for registration, based on the MI of image intensity and spatial location. Experimental results demonstrate the effectiveness of the marginalization formulation and the MI-based region selection technique for ultrasound (US) to magnetic resonance (MR) registration in an image-guided neurosurgical application.
Matthew Toews, William M. Wells III
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2009
Where IPMI
Authors Matthew Toews, William M. Wells III
Comments (0)
books