&lt;title&gt;Volumetric deformable models: active blobs&lt;/title&gt;

Whitaker, Ross T.

doi:10.1117/12.185173

Cited by 55 publications

(53 citation statements)

References 16 publications

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“…Deformable 3D surface models have been applied for segmentation and shape recovery from 3D range data [26] and 3D medical imaging data-sets [27]. A variety of shape representations have been explored to achieve this, including surface meshes [28], particle systems [29], superquadrics [30] and implicit representations [31]. Surface evolution has been realised through discretisation of the deformation energy functions using finite differences [28] and finite elements [32] or through level-set evolution of an implicit surface [33].…”

Section: Functional Modellingmentioning

confidence: 99%

Model-based human shape reconstruction from multiple views

Starck

Hilton

2008

Computer Vision and Image Understanding

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Section: Functional Modellingmentioning

confidence: 99%

Model-based human shape reconstruction from multiple views

Starck

Hilton

2008

Computer Vision and Image Understanding

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“…One choice of a prior that is described in the literature is to penalize the surface area, in which case the first variation is the mean curvature. An alternative prior [11], used in this paper, is to use a weighted sum of principal curvatures that does not penalize linear structures such as cylinders. Curvature motion is in the normal direction, and the surface motion becomes~6…”

Section: Mathematical Formulationmentioning

confidence: 99%

“…The literature describes many examples of level sets as curve and surface models for image segmentation [7], [10], [11], [12]. The authors have examined their usefulness for 3D segmentation of EMT reconstructions [1], the results of which are limited in quality due to reconstruction artifacts.…”

Section: Related Workmentioning

confidence: 99%

“…We use the sparse-field algorithm [9], which computes updates on a (relatively) small set of grid points, called the active layer, which is only one point wide, and can position level-set surface models to sub-voxel accuracy. 1 The curvature, ¢ , is computed as described in the literature [7], [11] using central differences on the discrete approximation to¨.…”

Section: The Level-set Deformationmentioning

confidence: 99%

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From Sinograms to Surfaces: A Direct Approach to the Segmentation of Tomographic Data

Elangovan

Whitaker

2001

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2001

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Abstract. Under ideal circumstances the problem of tomographic reconstruction is well-posed, and measured data are sufficient to obtain accurate estimates of volume densities. In such cases segmentation and surface estimation from the reconstructed volume are justified. In other situations the reconstructed volumes are not suitable for subsequent segmentation. This can happen in the case of incomplete sinograms, noise in the measurement process, or misregistration of the views. This paper presents a direct approach to the segmentation of incomplete and noisy tomographic data. The strategy is to impose a fairly simple model on the data, and treat segmentation as a problem of estimating the interface between two substances of somewhat homogeneous density. The segmentation is achieved by simultaneously deforming a surface model and updating density parameters in order to achieve a best fit between the projected model and the input sinograms. The deformation is implemented with level-set surface models, calculated at the resolution of the input data. Several computational innovations make the approach feasible with state-of-the-art computers. The usefulness of the approach is demonstrated by reconstructing the shape of spiny dendrites from electron microscope tomographic data.

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“…Using level set methods, several authors have proposed smoothing surfaces by weighted combinations of principal curvatures. For instance, Whitaker [23] proposes a nonlinear reweighting scheme that favors the smaller curvature and preserves cylindrical structures. Lorigo et al [24] propose a smoothing by the minimum curvature.…”

Section: Related Workmentioning

confidence: 99%

Higher-order nonlinear priors for surface reconstruction

Taşdizen

Whitaker

2004

IEEE Trans. Pattern Anal. Machine Intell.

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Abstract-For surface reconstruction problems with noisy and incomplete range data, a Bayesian estimation approach can improve the overall quality of the surfaces. The Bayesian approach to surface estimation relies on a likelihood term, which ties the surface estimate to the input data, and the prior, which ensures surface smoothness or continuity. This paper introduces a new high-order, nonlinear prior for surface reconstruction. The proposed prior can smooth complex, noisy surfaces, while preserving sharp, geometric features, and it is a natural generalization of edge-preserving methods in image processing, such as anisotropic diffusion. An exact solution would require solving a fourth-order partial differential equation (PDE), which can be difficult with conventional numerical techniques. Our approach is to solve a cascade system of two second-order PDEs, which resembles the original fourth-order system. This strategy is based on the observation that the generalization of image processing to surfaces entails filtering the surface normals. We solve one PDE for processing the normals and one for refitting the surface to the normals. Furthermore, we implement the associated surface deformations using level sets. Hence, the algorithm can accommodate very complex shapes with arbitrary and changing topologies. This paper gives the mathematical formulation and describes the numerical algorithms. We also show results using range and medical data.

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<title>Volumetric deformable models: active blobs</title>

Cited by 55 publications

References 16 publications

Model-based human shape reconstruction from multiple views

Model-based human shape reconstruction from multiple views

From Sinograms to Surfaces: A Direct Approach to the Segmentation of Tomographic Data

Higher-order nonlinear priors for surface reconstruction

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