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

Abstract

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.

Statistical Motion Mask and Sliding Registration

“…However, results with this spacing and w go = [0.8, 0.85, 0.9] are very close together and below 1.34mm. Compared with other methods, our resulting average landmark registration error is lower than those reported for instance by Papiez et al [10] (1.95mm), Wu et al [14] (1.47mm), Delmon et al [5] (1.66mm), and Berendsen et al [1] (1.36mm); but slightly larger the one reported by Hua et al [7] (1.17mm) or Vishnevskiy et al [13] (0.95mm). A comprehensive list of landmark errors achieved by various algorithms is published on the DIR-Lab website [11].…”

“…Several registration methods for handling sliding motion have been proposed in the literature [1,5,7,10,13,14]. Most of these methods require a segmentation of the sliding regions either in the target image or in both target and source image.…”

Multiresolution eXtended Free-Form Deformations (XFFD) for non-rigid registration with discontinuous transforms

“…The DIR-lab dataset was adopted in most of the previous methods treating discontinuities. We computed the same measurements, TRE, gap and overlap volumes, as reported in all the B-spline based methods (Wu et al, 2008;Delmon et al, 2013;Berendsen et al, 2014) and some diffusion-based methods (Schmidt-Richberg et al, 2012;Pace et al, 2013;Papież et al, 2014).…”

“…Direction-dependent regularizers were employed by decomposing the deformation field into normal and tangential directions at the discontinuity interface and smoothing was only applied in the tangential components but not across the boundary (Schmidt-Richberg et al, 2012;Pace et al, 2013). Locally adaptive regularizers were adopted with discontinuity preserving properties (Ruan et al, 2009;Papież et al, 2014).…”

“…We computed the Euclidean distance between the landmarks in the moving image and those in the target image, displaced by the deformation field. The mean and standard deviation of the distance of all the landmarks in each case was reported as the TRE Target Registration Error (TRE) (Schmidt-Richberg et al, 2012;Pace et al, 2013;Papież et al, 2014;Wu et al, 2008;Delmon et al, 2013;Berendsen et al, 2014).…”