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Chandler, A.G.* ; Netsch, T.* ; Cocosco, C.A.* ; Schnabel, J.A.* ; Hawkes, D.J.*

Slice-to-volume registration using mutual information between probabilistic image classifications.

In:. SPIE, 2004. 1120-1129 (Proc. SPIE ; 5370 II)
DOI
Open Access Green as soon as Postprint is submitted to ZB.
Intensity based registration algorithms have proved to be accurate and robust for 3D-3D registration tasks. However, these methods utilise the information content within an image, and therefore their performance is hindered for image data that is sparse. This is the case for the registration of a single image slice to a 3D image volume. There are some important applications that could benefit from improved slice-to-volume registration, for example, the planning of magnetic resonance (MR) scans or cardiac MR imaging, where images are acquired as stacks of single slices. We have developed and validated an information based slice-to-volume registration algorithm that uses vector valued probabilistic images of tissue classification that have been derived from the original intensity images. We believe that using such methods inherently incorporates into the registration framework more information about the images, especially in images containing severe partial volume artifacts. Initial experimental results indicate that the suggested method can achieve a more robust registration compared to standard intensity based methods for the rigid registration of a single thick brain MR slice, containing severe partial volume artifacts in the through-plane direction, to a complete 3D MR brain volume.
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Publication type Article: Conference contribution
Corresponding Author
Keywords C-means ; Fuzzy Classification ; Normalised Mutual Information ; Slice-to-volume Registration
ISSN (print) / ISBN 0277-786X
e-ISSN 1996-756X
Journal Proceedings of SPIE
Quellenangaben Volume: 5370 II, Issue: , Pages: 1120-1129 Article Number: , Supplement: ,
Publisher SPIE
Non-patent literature Publications
Reviewing status Peer reviewed
Institute(s) Institute for Machine Learning in Biomed Imaging (IML)