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Graph Cuts-Based Registration Revisited: A Novel Approach for Lung Image Registration Using Supervoxels and Image-Guided Filtering.
In: (2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 26 June 2016 - 01 July 2016, Las Vegas, NV, USA). 2016. 592-599 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops)
This work revisits the concept of graph cuts as an efficient optimization technique in image registration. Previously, due to the computational burden involved, the use of graph cuts in this context has been mainly limited to 2D applications. Here we show how combining graph cuts with supervoxels, resulting in a sparse, yet meaningful graph-based image representation, can overcome previous limitations. Additionally, we show that a relaxed graph representation of the image allows for 'sliding' motion modeling and provides anatomically plausible estimation of the deformations. This is achieved by using image-guided filtering of the estimated sparse deformation field. We evaluate our method on a publicly available CT lung data set and show that our new approach compares very favourably with state-of-the-art in continuous and discrete image registration.
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Publication type
Article: Conference contribution
Keywords
Discrete Optimization ; Graph Cuts ; Image Registration ; Image-guided Filtering ; Supervoxels
ISSN (print) / ISBN
2160-7508
e-ISSN
2160-7516
Conference Title
2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Conference Date
26 June 2016 - 01 July 2016
Conference Location
Las Vegas, NV, USA
Quellenangaben
Pages: 592-599
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Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)