Registration Using Sparse Free-Form Deformations

Shi, Wenzhe; Zhuang, Xiahai; Pizarro, Luis; Bai, Wenjia; Wang, Haiyan; Tung, Kai-Pin; Edwards, Philip; Rueckert, Daniel

doi:10.1007/978-3-642-33418-4_81

Cited by 25 publications

(27 citation statements)

References 22 publications

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“…The deformation of organs [109,275] or the estimation of blood flow [124] are examples of medical applications that can require optical flow computation. Many conceptual and methodological links can also be found between medical image registration and optical flow [100,211,212,219,224]. In microscopy, dense motion can inform about cell deformation [3,90,139,203], motion of cellular structures [73,89], or help for individual cell tracking [167].…”

Section: Applicative Domainsmentioning

confidence: 99%

“…Despite good results, the method is still limited by over-smoothing. The method of [219] addresses the discontinuity problem with a sparsity constraint on the B-spline coefficients, allowing to modulate the influence of the control points. Recent approaches [100,101,219] also add an explicit regularization on the motion field, overlapping with the methodology described in Section 5.…”

Section: Free-form Deformationsmentioning

confidence: 99%

“…As previously mentioned, the spatial adaptation of the parameters aðxÞ is a way to cope with complex and discontinuous flow fields [93,191,219]. This approach often involves a priori constraints on the spatial distribution of aðxÞ and is thus strongly related to the methods that will be presented in Section 5.…”

Section: Neighborhood Selectionmentioning

confidence: 99%

See 2 more Smart Citations

Optical flow modeling and computation: A survey

Fortun

Bouthémy

Kervrann

2015

Computer Vision and Image Understanding

372

182

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a b s t r a c tOptical flow estimation is one of the oldest and still most active research domains in computer vision. In 35 years, many methodological concepts have been introduced and have progressively improved performances, while opening the way to new challenges. In the last decade, the growing interest in evaluation benchmarks has stimulated a great amount of work. In this paper, we propose a survey of optical flow estimation classifying the main principles elaborated during this evolution, with a particular concern given to recent developments. It is conceived as a tutorial organizing in a comprehensive framework current approaches and practices. We give insights on the motivations, interests and limitations of modeling and optimization techniques, and we highlight similarities between methods to allow for a clear understanding of their behavior.

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Section: Applicative Domainsmentioning

confidence: 99%

Section: Free-form Deformationsmentioning

confidence: 99%

See 1 more Smart Citation

Optical flow modeling and computation: A survey

Fortun

Bouthémy

Kervrann

2015

Computer Vision and Image Understanding

372

182

View full text Add to dashboard Cite

show abstract

“…In Shi [8] was proposed a sparse Free Form Deformation for which fine local details such as motion discontinuities could be captured. In Yang [9], demons algorithm with locally adaptive regularization was proposed.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Free Form Deformation and Demons Registration with Discontinuities

et al. 2018

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Medical image registration plays an important part in most today's clinical procedures. Registration goal is to find transformation which warps one image into the space of another. Registration of moving organs in human body has a significant part in therapy planning. This task is harder in cases when one organ (tissue) slides along another, i.e. in a case of discontinuities in the motion field. Discontinuities introduce unwanted transformations which often lead to poor or unsatisfied registration results. In this paper we evaluate one form of discontinuities for two well-known and used registration algorithms namely Free Form Deformation and Demons.

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“…One main difficulty of the FFD approach is to cope with the conflict between global robustness and local accuracy [29,30] . Shi et al [30] defined the robustness as the ability to recover the deformation in the presence of noise, and the accuracy as the ability to reconstruct the highly-localized and potentially discontinuous deformation with as little error as possible.…”

Section: Introductionmentioning

confidence: 99%

A TV-l 1 based nonrigid image registration by coupling parametric and non-parametric transformation

Xie

et al. 2015

Int. J. Autom. Comput.

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Abstract:To overcome the conflict between the global robustness and the local accuracy in the dense nonrigid image registration, we propose a union registration approach using a l1-norm based term to couple the parametric transformation and the non-parametric transformation. On one hand, we take the parametric deformation field as a constraint for the non-parametric registration, which is a strong constraint that guarantees the robustness of the non-parametric registration result. On the other hand, the non-parametric deformation field is taken as a force to improve the accuracy of the parametric registration. Then, an alternating optimization scheme is carried out to improve the accuracy of both the parametric registration and the non-parametric registration. Moreover, accounting for the effect of spatially-varying intensity distortions and the sparse gradient prior of the deformation field, we adopt the residual complexity (RC) as the similarity metric and the total variation (TV) as the regularization. Because of the TV-l1-l2 composite property of the objective function in our union image registration, we resort to the split Bregman iteration for the complex problem solving. Experiments with both synthetic and real images in different domains illustrate that this approach outperforms the separately performed parametric registration or non-parametric registration.

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Registration Using Sparse Free-Form Deformations

Cited by 25 publications

References 22 publications

Optical flow modeling and computation: A survey

Optical flow modeling and computation: A survey

Evaluation of Free Form Deformation and Demons Registration with Discontinuities

A TV-l 1 based nonrigid image registration by coupling parametric and non-parametric transformation

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