Model-based curve evolution technique for image segmentation

Tsai, Andy; Yezzi, Anthony; Wells, William M.; Tempany, Clare M.; Tucker, David; Fan, Andy; Grimson, W. Eric L.; Willsky, Alan S.

doi:10.1109/cvpr.2001.990511

Cited by 131 publications

(152 citation statements)

References 8 publications

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“…Each object in the image can be segmented independently according to its shape prior and image gray level information. This formulation corresponds to previous work [7], [9].…”

Section: Whenmentioning

confidence: 99%

Neighbor-Constrained Segmentation With Level Set Based 3-D Deformable Models

Yang

Staib

Duncan

2004

IEEE Trans. Med. Imaging

125

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A novel method for the segmentation of multiple objects from three-dimensional (3-D) medical images using interobject constraints is presented. Our method is motivated by the observation that neighboring structures have consistent locations and shapes that provide configurations and context that aid in segmentation. We define a maximum a posteriori (MAP) estimation framework using the constraining information provided by neighboring objects to segment several objects simultaneously. We introduce a representation for the joint density function of the neighbor objects, and define joint probability distributions over the variations of the neighboring shape and position relationships of a set of training images. In order to estimate the MAP shapes of the objects, we formulate the model in terms of level set functions, and compute the associated EulerLagrange equations. The contours evolve both according to the neighbor prior information and the image gray level information. This method is useful in situations where there is limited interobject information as opposed to robust global atlases. In addition, we compare our level set representation of the object shape to the point distribution model. Results and validation from experiments on synthetic data and medical imagery in two-dimensional and 3-D are demonstrated.

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“…Each object in the image can be segmented independently according to its shape prior and image gray level information. This formulation corresponds to previous work [7], [9].…”

Section: Whenmentioning

confidence: 99%

Neighbor-Constrained Segmentation With Level Set Based 3-D Deformable Models

Yang

Staib

Duncan

2004

IEEE Trans. Med. Imaging

125

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“…Such PCA based representations of level set functions have been successfully applied for the construction of statistical shape priors in [12,21,17]. It should be pointed out that the application of PCA to the embedding function has certain limitations.…”

Section: A Compact Low-dimensional Representationmentioning

confidence: 99%

“…Leventon et al [12] modeled the embedding function by principal component analysis (PCA) of a set of training shapes and added appropriate driving terms to the level set evolution equation. Tsai et al [21] suggested a more efficient formulation, where optimization is performed directly within the subspace of the first few eigenmodes. Rousson et al [17] introduced shape information on the variational level.…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Dynamical Shape Priors for Level Set Segmentation

Cremers

2008

J Sci Comput

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The introduction of statistical shape knowledge into level set based segmentation methods was shown to improve the segmentation of familiar structures in the presence of noise, clutter or partial occlusions. While most work has been focused on shape priors which are constant in time, it is clear that when tracking deformable shapes certain silhouettes may become more or less likely over time. In fact, the deformations of familiar objects such as the silhouettes of a walking person are often characterized by pronounced temporal correlations.In this paper, we propose a nonlinear dynamical shape prior for level set based image segmentation. Specifically, we propose to approximate the temporal evolution of the eigenmodes of the level set function by means of a mixture of autoregressive models. We detail how such shape priors "with memory" can be integrated into a variational framework for level set segmentation. As an application, we experimentally validate that the nonlinear dynamical prior drastically improves the tracking of a person walking in different directions, despite large amounts of clutter and noise.

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“…Independent of each other, Chan and Vese [3] and Tsai et al [21] proposed level set implementations of the Mumford-Shah functional. The boundary between object and background is represented by the zero-level set of a signed distance function ϕ : Ω → R [15].…”

Section: Introductionmentioning

confidence: 99%

Interactive Image Segmentation Using Level Sets and Dempster-Shafer Theory of Evidence

Scheuermann

Rosenhahn

2011

Lecture Notes in Computer Science

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Abstract.Variational frameworks based on level set methods are popular for the general problem of image segmentation. They combine different feature channels in an energy minimization approach. In contrast to other popular segmentation frameworks, e.g. the graph cut framework, current level set formulations do not allow much user interaction. Except for selecting the initial boundary, the user is barely able to guide or correct the boundary propagation. Based on Dempster-Shafer theory of evidence we propose a segmentation framework which integrates user interaction in a novel way. Given the input image, the proposed algorithm determines the best segmentation allowing the user to take global influence on the boundary propagation.

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Model-based curve evolution technique for image segmentation

Cited by 131 publications

References 8 publications

Neighbor-Constrained Segmentation With Level Set Based 3-D Deformable Models

Neighbor-Constrained Segmentation With Level Set Based 3-D Deformable Models

Nonlinear Dynamical Shape Priors for Level Set Segmentation

Interactive Image Segmentation Using Level Sets and Dempster-Shafer Theory of Evidence

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