NDLI: A contextual maximum likelihood framework for modeling image registration

Content Provider	IEEE Xplore Digital Library
Author	Wachinger, C. Navab, N.
Copyright Year	2012
Description	Author affiliation: Computer Aided Medical Procedures, Technische Universität München, Germany (Wachinger, C.; Navab, N.)
Abstract	We introduce a novel probabilistic framework for image registration. This framework considers, in contrast to previous ones, local neighborhood information. We integrate the neighborhood information into the framework by adding layers of latent random variables, characterizing the descriptive information of each image. This extension has multiple advantages. It allows for a unified description of geometric and iconic registration, with the consequential analysis of similarities. It enables to arrange registration techniques in a continuum, limited by pure intensity-and feature-based registration. With this wide spectrum of techniques combined, we can model hybrid registration approaches. The probabilistic coupling allows further to deduce optimal descriptors and to model the adaptation of description layers during the process, as it is done for joint registration/segmentation. Finally, we deduce a new registration algorithm that allows for a dynamic adaptation of the description layers during the registration. Excellent results confirm the advantages of the new registration method, the major contribution of this article lies, however, in the theoretical analysis.
Starting Page	1995
Ending Page	2002
File Size	192208
Page Count	8
File Format	PDF
ISBN	9781467312264
ISSN	10636919
e-ISBN	9781467312288
e-ISBN	9781467312271
DOI	10.1109/CVPR.2012.6247902
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-06-16
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Probabilistic logic Mathematical model Random variables Graphical models Estimation Context Equations
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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