Stent graft removal for improving 2D&amp;#x2013;3D registration

Baust, Maximilian; Demirci, Stefanie; Navab, Nassir

doi:10.1109/isbi.2009.5193277

Cited by 1 publication

(2 citation statements)

References 6 publications

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“…More precisely, we consider the problem of images being distorted by severe artifacts. An application where the registration of such images is required is endovascular stent graft placement, where the bright stent is only present in the intra-operative radiograph [3]. Instead of pre-processing images as in [3], we deduce a registration algorithm that is robust to such artifacts.…”

Section: Registration With Dynamic Adaptationmentioning

confidence: 99%

“…An application where the registration of such images is required is endovascular stent graft placement, where the bright stent is only present in the intra-operative radiograph [3]. Instead of pre-processing images as in [3], we deduce a registration algorithm that is robust to such artifacts. This is advantageous because we retain all the information during the registration and the algorithm identifies artifacts automatically.…”

Section: Registration With Dynamic Adaptationmentioning

confidence: 99%

See 1 more Smart Citation

A contextual maximum likelihood framework for modeling image registration

Wachinger

Navab

2012

2012 IEEE Conference on Computer Vision and Pattern Recognition

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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 intensityand 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.

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Section: Registration With Dynamic Adaptationmentioning

confidence: 99%

Section: Registration With Dynamic Adaptationmentioning

confidence: 99%