Elastic registration of medical images using radial basis functions with compact support

Fornefett, Mike; Rohr, Karl; Stiehl, H. Siegfried

doi:10.1109/cvpr.1999.786970

Cited by 54 publications

(40 citation statements)

References 8 publications

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“…Figure 6(g),6(h) ). Figure 7 depicts the mesh deformation using the error function defined in equation (10). To illustrate the mapping result we use the contour of the logo that we find using a simple gradient operator and overlay the contour of the deformed model (in blue in Figure 7) with the contour of the target object (in red in Figure 7).…”

Section: Resultsmentioning

confidence: 99%

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Deformable object tracking using optical flow constraints

Hilsmann

Eisert

2007

IET 4th European Conference on Visual Media Production (CVMP 2007)

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In this paper, we present a method for deformable object tracking that exploits the entire image information using the optical flow equation instead of working with discrete feature points. Our method starts from the optical flow constraint and first estimates global transformations in a hierarchical framework. Elastic deformations are then estimated separately using deformable meshes and spatial and temporal smoothing constraints. In both cases additional constraints to regularize the optical flow field are obtained from the motion model such that finding the best transformation amounts in minimizing an error function that can be solved in a least squares sense. Combing deformable meshes and the optical flow equation with a dedicated weighted smoothness constraint on the mesh deformation and estimating global transformations separately from elastic deformations is key to dealing with complex deformations such as cloth deformation as a person moves.

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Section: Resultsmentioning

confidence: 99%

“…If the set of points is large enough also more complex transformations can be determined using e.g. radial basis functions (RBF) [3], [10]. The type of the RBF determines overall characteristics of the transformation such as the smoothness or the locality.…”

Section: Related Workmentioning

confidence: 99%

Deformable object tracking using optical flow constraints

Hilsmann

Eisert

2007

IET 4th European Conference on Visual Media Production (CVMP 2007)

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“…The alternative possibilities that come to mind are polynomials [45], harmonic functions [18], [46], radial basis functions [3], [47], and wavelets [35], [48], [49].…”

Section: Deformation Basismentioning

confidence: 99%

Fast parametric elastic image registration

Kybic

Unser

2003

IEEE Trans. on Image Process.

403

284

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Abstract-We present an algorithm for fast elastic multidimensional intensity-based image registration with a parametric model of the deformation. It is fully automatic in its default mode of operation. In the case of hard real-world problems, it is capable of accepting expert hints in the form of soft landmark constraints. Much fewer landmarks are needed and the results are far superior compared to pure landmark registration. Particular attention has been paid to the factors influencing the speed of this algorithm. The B-spline deformation model is shown to be computationally more efficient than other alternatives.The algorithm has been successfully used for several two-dimensional (2-D) and three-dimensional (3-D) registration tasks in the medical domain, involving MRI, SPECT, CT, and ultrasound image modalities. We also present experiments in a controlled environment, permitting an exact evaluation of the registration accuracy. Test deformations are generated automatically using a random hierarchical fractional wavelet-based generator.

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“…Methods based on local basis expansions of the deformation such as B-splines [22,23] or Radial Basis Functions [24,25] provide a suitable compromise between global and fluid models. As will be seen in the subsequent sections, we use a finite element registration framework based on a partition of unity, which enables to easily control the globality of ψ.…”

Section: Deformation Modelsmentioning

confidence: 99%

Prior-Based Piecewise-Smooth Segmentation by Template Competitive Deformation Using Partitions of Unity

Somphone

Mory

Makram–Ebeid

et al. 2008

Lecture Notes in Computer Science

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Abstract. We propose a new algorithm for two-phase, piecewise-smooth segmentation with shape prior. The image is segmented by a binary template that is deformed by a regular geometric transformation. The choice of the template together with the constraint on the transformation introduce the shape prior. In particular, the topology of the shape is preserved if the transformation is diffeomorphic. The deformation is guided by the maximization of the likelihood of foreground and background intensity models, so that we can refer to this approach as Competitive Deformation. In each region, the intensity is modelled as a smooth approximation of the original image. We represent the transformation using a Partition of Unity Finite Element Method, which consists in representing each component with polynomial approximations within local patches. A conformity constraint between the patches provides a way to control the globality of the deformation. We show several results on synthetic images, as well as on medical data from different modalities.

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Elastic registration of medical images using radial basis functions with compact support

Abstract: Abstract

Cited by 54 publications

References 8 publications

Deformable object tracking using optical flow constraints

Deformable object tracking using optical flow constraints

Fast parametric elastic image registration

Prior-Based Piecewise-Smooth Segmentation by Template Competitive Deformation Using Partitions of Unity

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