Discontinuity-preserving optical flow algorithm

Yuan, Liangjie; Jin-zong, LI; Li, Dongdong

doi:10.1016/s1004-4132(07)60097-8

Cited by 13 publications

(6 citation statements)

References 9 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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“…Computations were performed using Matlab 2011a on a processor of Intel(R) core TM i7 − 2670QM CPU@ 2.20GHz with 6.00 GB of installed RAM. Here, two types of texture are used: stochastic, which is generated according to (11) and regular, which is taken from a texture image. The reason for considering regular texture is that as can be seen in the fourth column of Table 3, computation time for the method in [6] has increased when stochastic texture is added to sequences.…”

Section: Sequencementioning

confidence: 99%

Effects of texture addition on optical flow performance in images with poor texture

Andalibi

Hoberock

Mohamadipanah

2015

Image and Vision Computing

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“…Optical flow techniques are divided into three main categories: the differential techniques, the frequency-based ones, and the matching methods [15]. The chosen calculation method is a differential one, that is, the classical Horn and Schunk optical flow model as modified in [19].…”

Section: Motion Field Extractionmentioning

confidence: 99%

A Recursive Fuzzy System for Efficient Digital Image Stabilization

Kyriakoulis

Γαστεράτος

2008

Advances in Fuzzy Systems

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A novel digital image stabilization technique is proposed in this paper. It is based on a fuzzy Kalman compensation of the global motion vector (GMV), which is estimated in the log-polar plane. The GMV is extracted using four local motion vectors (LMVs) computed on respective subimages in the logpolar plane. The fuzzy Kalman system consists of a fuzzy system with the Kalman filter's discrete time-invariant definition. Due to this inherited recursiveness, the output results into smoothed image sequences. The proposed stabilization system aims to compensate any oscillations of the frame absolute positions, based on the motion estimation in the log-polar domain, filtered by the fuzzy Kalman system, and thus the advantages of both the fuzzy Kalman system and the log-polar transformation are exploited. The described technique produces optimal results in terms of the output quality and the level of compensation.

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Discontinuity-preserving optical flow algorithm

Cited by 13 publications

References 9 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

Effects of texture addition on optical flow performance in images with poor texture

A Recursive Fuzzy System for Efficient Digital Image Stabilization

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