Self-Repelling Snakes for Topology-Preserving Segmentation Models

Guyader, Carole Le; Vese, Luminita A.

doi:10.1109/tip.2008.919951

Cited by 55 publications

(42 citation statements)

References 42 publications

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“…2 illustrates how the method can be used in the case of topology-preserving segmentation ( [16], [1], [28], [17] on this topic). This synthetic reference image represents two disks (similar to tests performed in prior related works [16], [28], [17]). The template image, defined on the same image domain is made of a black ellipse such that, when superimposed on the reference image, its boundary encloses the two disks.…”

Section: Numerical Experimentsmentioning

confidence: 99%

A Combined Segmentation and Registration Framework with a Nonlinear Elasticity Smoother

Guyader¹,

Vese²

2009

Lecture Notes in Computer Science

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Abstract. In this paper, we present a new non-parametric combined segmentation and registration method. The problem is cast as an optimization one, combining a matching criterion based on the active contour without edges [4] for segmentation, and a nonlinear-elasticity-based smoother on the displacement vector field. This modeling is twofold: first, registration is jointly performed with segmentation since guided by the segmentation process; it means that the algorithm produces both a smooth mapping between the two shapes and the segmentation of the object contained in the reference image. Secondly, the use of a nonlinearelasticity-type regularizer allows large deformations to occur, which makes the model comparable in this point with the viscous fluid registration method [7]. Several applications are proposed to demonstrate the potential of this method to both segmentation of one single image and to registration between two images.

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Section: Numerical Experimentsmentioning

confidence: 99%

A Combined Segmentation and Registration Framework with a Nonlinear Elasticity Smoother

Guyader¹,

Vese²

2009

Lecture Notes in Computer Science

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“…While using a parametric model or geometric model depends on the concrete segmentation task. The topological flexibility is not always a desirable feature under some applications (Han, et al, 2003;Guyader and Vese, 2008). In general, when structures have large shape variety or complicated topology, geometric deformable models are preferred; when the interested structures have open boundaries or the structures are thin or the algorithms need real-time operations, parametric models are preferred.…”

Section: Discussionmentioning

confidence: 99%

A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

Tavares

Jorge

et al. 2010

Computer Methods in Biomechanics and Biomedical Engineering

270

142

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This paper aims to make a review on the current segmentation algorithms used for medical images.Algorithms are classified according to their principal methodologies, namely the ones based on thresholds, the ones based on clustering techniques and the ones based on deformable models. The last type is focused due to the intensive investigations on the deformable models that have been done in the last few decades. Typical algorithms of each type are discussed and the main ideas, application fields, advantages and disadvantages of each type are summarized. Experiments that apply these algorithms to segment the organs and tissues of the female pelvic cavity are presented to further illustrate their distinct characteristics. In the end the main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed.

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“…Among the family of topology constrained front propagation methods [35,5,174,105,78,157], the works in [78] and in [157] rely on simple points [13,90,14], that is, points such that their addition or removal to the component will not change the topology of the image. They start from initial seeds distributed in the areas of interest in the space of the image, and then modify these components by adding or removing simple points.…”

Section: Front Propagation and Well-composed Segmentationsmentioning

confidence: 99%

A Tutorial on Well-Composedness

2017

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Due to digitization, usual discrete signals generally present topological paradoxes, such as the connectivity paradoxes of Rosenfeld. To get rid of those paradoxes, and to restore some topological properties to the objects contained in the image, like manifoldness, Latecki proposed a new class of images, called wellcomposed images, with no topological issues. Furthermore, well-composed images have some other interesting properties: for example, the Euler number is locally computable, boundaries of objects separate background from foreground, the tree of shapes is well-defined, and so on. Last, but not the least, some recent works in mathematical morphology have shown that very nice practical results can be obtained thanks to well-composed images. Believing in its prime importance in digital topology, we then propose this state-of-the-art of well-composedness, summarizing its different flavours, the different methods existing to produce well-composed signals, and the various topics that are related to well-composedness.

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Self-Repelling Snakes for Topology-Preserving Segmentation Models

Cited by 55 publications

References 42 publications

A Combined Segmentation and Registration Framework with a Nonlinear Elasticity Smoother

A Combined Segmentation and Registration Framework with a Nonlinear Elasticity Smoother

A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

A Tutorial on Well-Composedness

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