MIDL 2021

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Medical Imaging with Deep Learning
5. bis 7. Juli 2021

An implicit sliding-motion preserving regularisation via bilateral filtering for deformable image registration.

TitleAn implicit sliding-motion preserving regularisation via bilateral filtering for deformable image registration.
Publication TypeJournal Article
Year of Publication2014
AuthorsPapież B.W., Heinrich M.P., Fehrenbach J., Risser L., Schnabel J.A.
JournalMedical image analysis
Date Published2014 Dec
Publication Languageeng
KeywordsAlgorithms, Artificial Intelligence, Four-Dimensional Computed Tomography, Humans, Lung, Motion, Pattern Recognition, Automated, Radiographic Image Enhancement, Radiographic Image Interpretation, Computer-Assisted, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique

Several biomedical applications require accurate image registration that can cope effectively with complex organ deformations. This paper addresses this problem by introducing a generic deformable registration algorithm with a new regularization scheme, which is performed through bilateral filtering of the deformation field. The proposed approach is primarily designed to handle smooth deformations both between and within body structures, and also more challenging deformation discontinuities exhibited by sliding organs. The conventional Gaussian smoothing of deformation fields is replaced by a bilateral filtering procedure, which compromises between the spatial smoothness and local intensity similarity kernels, and is further supported by a deformation field similarity kernel. Moreover, the presented framework does not require any explicit prior knowledge about the organ motion properties (e.g. segmentation) and therefore forms a fully automated registration technique. Validation was performed using synthetic phantom data and publicly available clinical 4D CT lung data sets. In both cases, the quantitative analysis shows improved accuracy when compared to conventional Gaussian smoothing. In addition, we provide experimental evidence that masking the lungs in order to avoid the problem of sliding motion during registration performs similarly in terms of the target registration error when compared to the proposed approach, however it requires accurate lung segmentation. Finally, quantification of the level and location of detected sliding motion yields visually plausible results by demonstrating noticeable sliding at the pleural cavity boundaries.

PubMed Link


Alternate JournalMed Image Anal
Created at October 26, 2015 - 10:33am.


Program of Study

Study Medical Informatics
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