Robust Discontinuity Preserving Optical Flow Methods

Monzón, Nelson; Salgado, Agustín; Sánchez, Javier

doi:10.5201/ipol.2016.172

Cited by 9 publications

(6 citation statements)

References 16 publications

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“…For example, while smoothness as a constraint of the estimated motion correctly penalizes shearing motion within organs and muscles, it also does so at organ boundaries, where this type of motion patterns frequently occurs, in particular in-between soft-tissue interfaces subject to cardiac or respiratory motion. In the context of the original Horn-Schunck algorithm, the problem to address motion discontinuities at object boundaries motivated several proposed amendments to the constraints (see for example (Terzopoulos 1986, Nessi 1993, Weickert & Schnör 2001a, Lei et al 2007, Monzón et al 2016 or to employ a spatio-temporal regularization (see (Nagel 1990, Black & Anandan 1991, Weickert & Schnör 2001b). A rather interesting approach was thereby pursued by Yang et al (Yang et al 2000), in the context of analyzing satellite image observations of ocean currents, with a variational based on the optical-flow principle.…”

Section: Introductionmentioning

confidence: 99%

Anatomically plausible models and quality assurance criteria for online mono- and multi-modal medical image registration

et al. 2018

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Medical imaging is currently employed in the diagnosis, planning, delivery and response monitoring of cancer treatments. Due to physiological motion and/or treatment response, the shape and location of the pathology and organs-at-risk may change over time. Establishing their location within the acquired images is therefore paramount for an accurate treatment delivery and monitoring. A feasible solution for tracking anatomical changes during an image-guided cancer treatment is provided by image registration algorithms. Such methods are, however, often built upon elements originating from the computer vision/graphics domain. Since the original design of such elements did not take into consideration the material properties of particular biological tissues, the anatomical plausibility of the estimated deformations may not be guaranteed. In the current work we adapt two existing variational registration algorithms, namely Horn-Schunck and EVolution, to online soft tissue tracking. This is achieved by enforcing an incompressibility constraint on the estimated deformations during the registration process. The existing and the modified registration methods were comparatively tested against several quality assurance criteria on abdominal in vivo MR and CT data. These criteria included: the Dice similarity coefficient (DSC), the Jaccard index, the target registration error (TRE) and three additional criteria evaluating the anatomical plausibility of the estimated deformations. Results demonstrated that both the original and the modified registration methods have similar registration capabilities in high-contrast areas, with DSC and Jaccard index values predominantly in the 0.8-0.9 range and an average TRE of 1.6-2.0 mm. In contrast-devoid regions of the liver and kidneys, however, the three additional quality assurance criteria have indicated a considerable improvement of the anatomical plausibility of the deformations estimated by the incompressibility-constrained methods. Moreover, the proposed registration models maintain the potential of the original methods for online image-based guidance of cancer treatments.

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Section: Introductionmentioning

confidence: 99%

Anatomically plausible models and quality assurance criteria for online mono- and multi-modal medical image registration

et al. 2018

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“…Therefore, the numerical scheme in (46) and the discretizations in (47) and (48) can be replicated. As shown in [16], and implemented in [17], a good choice for λ(x) is given by λ(x) := − ln(ξ) + ln(α) |∇u(x)| .…”

Section: A3 Numerical Scheme Of Discontinuity-preserving Robust Optimentioning

confidence: 99%

“…This article is organized as follows: Section 2 describes and justifies our assessment methodology and the new metrics employed. Section 3 describes the 1D optical flow algorithms employed in this study, in particular, the methods of Lucas and Kanade [13], Brox et al [3] and Monzón et al [17]. Section 4 deals with the set up of the experimental protocol.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Optical Flow Methods under Stereomatching with Short Baselines

Dagobert¹,

Monzón²,

Sánchez³

2019

Image Processing on Line

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This article studies the effectiveness of optical flow methods applied to short baseline image pairs under different noise levels. New metrics have been developed to analyze the results because the usual metrics are inadequate in a subpixel context. We have used the implementation of some standard optical flow methods adapted to the stereo problem. Our experiments show that the Brox et al. method produces the least errors, with a 60% success rate and a relative precision at 1/100th of a pixel. On the other hand, our comparison shows that a discontinuity preserving method, derived from Brox et al., also provides competitive results at the same time that it yields disparities with more details and correct contours. Source Code Source codes of Lucas-Kanade 1D, Robust Optical Flow 1D and Robust Discontinuity Preserving 1D algorithms are provided in the web page of the article 1 .

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“…Due to the size of the spatial search window, the optical flow does not need to be accurate. We have tested with two optical flow algorithms: the TV-L1 method [60] (we used the implementation of [53]), and its robust variant described [43], and have found virtually no difference in the denoising result. There is also no significant difference between computing the optical flow on the noisy data, or using an oracular optical flow computed on the ground truth.…”

Section: Search Regionmentioning

confidence: 99%

Video Denoising via Empirical Bayesian Estimation of Space-Time Patches

Arias

Morel

2017

J Math Imaging Vis

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In this paper we present a new patch-based empirical Bayesian video denoising algorithm. The method builds a Bayesian model for each group of similar space-time patches. These patches are not motion compensated, and therefore avoid the risk of inaccuracies caused by motion estimation errors. The high dimensionality of spatio-temporal patches together with a limited number of available samples, poses challenges when estimating the statistics needed for an empirical Bayesian method. We therefore assume that groups of similar patches have a low intrinsic dimensionality, leading to a spiked covariance model. Based on theoretical results about the estimation of spiked covariance matrices, we propose estimators of the eigenvalues of the a priori covariance in high dimensional spaces as simple corrections of the eigenvalues of the sample covariance matrix. We demonstrate empirically that these estimators lead to better empirical Wiener filters. A comparison on classic benchmark videos demonstrates improved visual quality and an increased PSNR with respect to state-of-the-art video denoising methods.

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Robust Discontinuity Preserving Optical Flow Methods

Cited by 9 publications

References 16 publications

Anatomically plausible models and quality assurance criteria for online mono- and multi-modal medical image registration

Anatomically plausible models and quality assurance criteria for online mono- and multi-modal medical image registration

Comparison of Optical Flow Methods under Stereomatching with Short Baselines

Video Denoising via Empirical Bayesian Estimation of Space-Time Patches

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