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Proceedings ArticleDOI

Registration of Temporal Ultrasonic Image Sequences Using Markov Random Fields

TL;DR: An approach to account for non-linear motion using a markov random field (MRF) based optimization scheme for registration is presented and it is shown that the method is suited to include prior knowledge about the data as the MRF system is able to model dependencies between the parameters of the optimization process.
Abstract: Ultrasound perfusion imaging is a rapid and inexpensive technique which enables observation of a dynamic process with high temporal resolution. The image acquisition is disturbed by various motion influences due to the acquisition procedure and patient motion. To extract valid information about perfusion for quantification and diagnostic purposes this influence must be compensated. In this work an approach to account for non-linear motion using a markov random field (MRF) based optimization scheme for registration is presented. Optimal transformation parameters are found all at once in a single optimization framework. Spatial and temporal constraints ensure continuity of a displacement field which is used for image transformation. Simulated datasets with known transformation fields are used to evaluate the presented method and demonstrate the potential of the system. Experiments with patient datasets show that superior results could be achieved compared to a pairwise image registration approach. Furthermore, it is shown that the method is suited to include prior knowledge about the data as the MRF system is able to model dependencies between the parameters of the optimization process.
Citations
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Journal ArticleDOI
TL;DR: A computer-aided method is proposed for objective and convenient quantification of contrast agent spatial distribution within plaques in CEUS image sequences including cardiac cycle retrieval and sub-sequence selection, temporal mean image segmentation, and texture feature extraction.

29 citations

Journal ArticleDOI
TL;DR: The proposed approach decreased the mean average difference between the measured perfusion and the pharmacokinetic model estimation and reduced the analysis time by 41% compared to manual processing.

13 citations


Cites methods from "Registration of Temporal Ultrasonic..."

  • ...In previous publications, we proposed the motion analysis for out-of-plane motion detection [13] and the MRF-based motion compensation system [33]....

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Journal ArticleDOI
01 Oct 2016-Strain
TL;DR: In this article, a new automatic method to estimate the 2D displacements and strains based on an existing tracking algorithm combined with filtering, regression and integration steps is described. But the results are promising, in particular for 2D displacement and to some extent for filtered strain.
Abstract: Ultrafast ultrasound imaging allows observing the internal response of soft tissues subjected to impacts. However, the limited image quality and the large deformations associated with such loading make the generation of displacement and strain maps difficult. This study describes a new automatic method to estimate the 2D displacements and strains based on an existing tracking algorithm combined with filtering, regression and integration steps. The predictions were assessed using four test cases: (1) Predicted displacements were compared with expert tracking from past tests, (2) predicted displacements and strains were compared with the known field used to deform numerically an image, (3) internal strains predicted based on ultrafast ultrasound were compared with strains provided by digital image correlation on nearby external surfaces, and (4) for almost noise-free images that contain random patterns, predicted strains were compared with results provided by a commercial digital image correlation software package. The results are promising, in particular for 2D displacements and, to some extent, for filtered strain. This suggests that the method could be useful to interpret ultrafast ultrasound data collected for application in impact biomechanics.

1 citations

References
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Journal ArticleDOI
Julian Besag1
TL;DR: In this paper, the authors proposed an iterative method for scene reconstruction based on a non-degenerate Markov Random Field (MRF) model, where the local characteristics of the original scene can be represented by a nondegenerate MRF and the reconstruction can be estimated according to standard criteria.
Abstract: may 7th, 1986, Professor A. F. M. Smith in the Chair] SUMMARY A continuous two-dimensional region is partitioned into a fine rectangular array of sites or "pixels", each pixel having a particular "colour" belonging to a prescribed finite set. The true colouring of the region is unknown but, associated with each pixel, there is a possibly multivariate record which conveys imperfect information about its colour according to a known statistical model. The aim is to reconstruct the true scene, with the additional knowledge that pixels close together tend to have the same or similar colours. In this paper, it is assumed that the local characteristics of the true scene can be represented by a nondegenerate Markov random field. Such information can be combined with the records by Bayes' theorem and the true scene can be estimated according to standard criteria. However, the computational burden is enormous and the reconstruction may reflect undesirable largescale properties of the random field. Thus, a simple, iterative method of reconstruction is proposed, which does not depend on these large-scale characteristics. The method is illustrated by computer simulations in which the original scene is not directly related to the assumed random field. Some complications, including parameter estimation, are discussed. Potential applications are mentioned briefly.

4,490 citations


"Registration of Temporal Ultrasonic..." refers background in this paper

  • ...The overall MRF energy must be minimized to obtain the global best configuration of the system [Bes86, GPK∗07]....

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  • ...The Hammersley-Clifford theorem [Bes86] stipulates the random variables X to be a MRF with respect to a neighborhood N if and only if the probability distribution of P(X ) is a Gibbs distribution: P(X ) = Z−1× e−U(X )....

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Journal ArticleDOI
TL;DR: This paper compares the running times of several standard algorithms, as well as a new algorithm that is recently developed that works several times faster than any of the other methods, making near real-time performance possible.
Abstract: Minimum cut/maximum flow algorithms on graphs have emerged as an increasingly useful tool for exactor approximate energy minimization in low-level vision. The combinatorial optimization literature provides many min-cut/max-flow algorithms with different polynomial time complexity. Their practical efficiency, however, has to date been studied mainly outside the scope of computer vision. The goal of this paper is to provide an experimental comparison of the efficiency of min-cut/max flow algorithms for applications in vision. We compare the running times of several standard algorithms, as well as a new algorithm that we have recently developed. The algorithms we study include both Goldberg-Tarjan style "push -relabel" methods and algorithms based on Ford-Fulkerson style "augmenting paths." We benchmark these algorithms on a number of typical graphs in the contexts of image restoration, stereo, and segmentation. In many cases, our new algorithm works several times faster than any of the other methods, making near real-time performance possible. An implementation of our max-flow/min-cut algorithm is available upon request for research purposes.

4,463 citations

Journal ArticleDOI
01 Jan 2004
TL;DR: This work gives a precise characterization of what energy functions can be minimized using graph cuts, among the energy functions that can be written as a sum of terms containing three or fewer binary variables.
Abstract: In the last few years, several new algorithms based on graph cuts have been developed to solve energy minimization problems in computer vision. Each of these techniques constructs a graph such that the minimum cut on the graph also minimizes the energy. Yet, because these graph constructions are complex and highly specific to a particular energy function, graph cuts have seen limited application to date. In this paper, we give a characterization of the energy functions that can be minimized by graph cuts. Our results are restricted to functions of binary variables. However, our work generalizes many previous constructions and is easily applicable to vision problems that involve large numbers of labels, such as stereo, motion, image restoration, and scene reconstruction. We give a precise characterization of what energy functions can be minimized using graph cuts, among the energy functions that can be written as a sum of terms containing three or fewer binary variables. We also provide a general-purpose construction to minimize such an energy function. Finally, we give a necessary condition for any energy function of binary variables to be minimized by graph cuts. Researchers who are considering the use of graph cuts to optimize a particular energy function can use our results to determine if this is possible and then follow our construction to create the appropriate graph. A software implementation is freely available.

3,079 citations

Journal ArticleDOI
TL;DR: Applications of image registration include combining images of the same subject from different modalities, aligning temporal sequences of images to compensate for motion of the subject between scans, image guidance during interventions and aligning images from multiple subjects in cohort studies.
Abstract: Radiological images are increasingly being used in healthcare and medical research. There is, consequently, widespread interest in accurately relating information in the different images for diagnosis, treatment and basic science. This article reviews registration techniques used to solve this problem, and describes the wide variety of applications to which these techniques are applied. Applications of image registration include combining images of the same subject from different modalities, aligning temporal sequences of images to compensate for motion of the subject between scans, image guidance during interventions and aligning images from multiple subjects in cohort studies. Current registration algorithms can, in many cases, automatically register images that are related by a rigid body transformation (i.e. where tissue deformation can be ignored). There has also been substantial progress in non-rigid registration algorithms that can compensate for tissue deformation, or align images from different subjects. Nevertheless many registration problems remain unsolved, and this is likely to continue to be an active field of research in the future.

2,166 citations

Journal ArticleDOI
01 Feb 2008
TL;DR: EFSUMB study group M. Claudon, D. Cosgrove, T. Tranquart, L. Thorelius, and H. Whittingham study group L. de.
Abstract: EFSUMB study group M. Claudon1, D. Cosgrove2, T. Albrecht3, L. Bolondi4, M. Bosio5, F. Calliada6, J.-M. Correas7, K. Darge8, C. Dietrich9, M. D'On ofrio10, D. H. Evans11, C. Filice12, L. Greiner13, K. Jäger14, N. de. Jong15, E. Leen16, R. Lencioni17, D. Lindsell18, A. Martegani19, S. Meairs20, C. Nolsøe21, F. Piscaglia22, P. Ricci23, G. Seidel24, B. Skjoldbye25, L. Solbiati26, L. Thorelius27, F. Tranquart28, H. P. Weskott29, T. Whittingham30

755 citations