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An implicit sliding-motion preserving regularisation via bilateral filtering for deformable image registration.
Med. Image Anal. 18, 1299-1311 (2014)
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.
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Anmerkungen
Besondere Publikation
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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Schlagwörter
Bilateral Filtering ; Nonrigid Registration ; Regularisation ; Respiratory Motion ; Sliding Motion
Sprache
englisch
Veröffentlichungsjahr
2014
HGF-Berichtsjahr
2014
ISSN (print) / ISBN
1361-8415
e-ISSN
1361-8415
Zeitschrift
Medical Image Analysis
Quellenangaben
Band: 18,
Heft: 8,
Seiten: 1299-1311
Verlag
Elsevier
Begutachtungsstatus
Peer reviewed
Institut(e)
Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s)
30205 - Bioengineering and Digital Health
Forschungsfeld(er)
Enabling and Novel Technologies
PSP-Element(e)
G-507100-001
Scopus ID
84926278292
PubMed ID
24968741
Erfassungsdatum
2022-09-06