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Szmul, A.* ; Papiez, B.W.* ; Hallack, A.* ; Grau, V.* ; Schnabel, J.A.*

Supervoxels for graph cuts-based deformable image registration using guided image filtering.

J. Electron. Imaging 26:061607 (2017)
DOI PMC
We propose combining a supervoxel-based image representation with the concept of graph cuts as an efficient optimization technique for three-dimensional (3-D) deformable image registration. Due to the pixels/voxels-wise graph construction, the use of graph cuts in this context has been mainly limited to two-dimensional (2-D) applications. However, our work overcomes some of the previous limitations by posing the problem on a graph created by adjacent supervoxels, where the number of nodes in the graph is reduced from the number of voxels to the number of supervoxels. We demonstrate how a supervoxel image representation combined with graph cuts-based optimization can be applied to 3-D data. We further show that the application of a relaxed graph representation of the image, followed by guided image filtering over the estimated deformation field, allows us to model "sliding motion." Applying this method to lung image registration results in highly accurate image registration and anatomically plausible estimations of the deformations. Evaluation of our method on a publicly available computed tomography lung image dataset leads to the observation that our approach compares very favorably with state of the art methods in continuous and discrete image registration, achieving target registration error of 1.16 mm on average per landmark.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Korrespondenzautor
Schlagwörter Graph Cuts ; Guided Image Filtering ; Image Registration ; Lung Motion ; Supervoxels
ISSN (print) / ISBN 1017-9909
e-ISSN 1560-229X
Zeitschrift Journal of Electronic Imaging
Quellenangaben Band: 26, Heft: 6, Seiten: , Artikelnummer: 061607 Supplement: ,
Verlag SPIE
Nichtpatentliteratur Publikationen
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)