Application of the Level-Set Model with Constraints in Image Segmentation

Klement, Vladimír; Oberhuber, Tomáš; Ševčovič, Daniel

doi:10.4208/nmtma.2015.m1418

Cited by 4 publications

(5 citation statements)

References 20 publications

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“…By considering the results from both Table 2 and Table 3, we conclude that using the numerical scheme (3.7) with the UI distribution can most effectively optimize the length and area. The experimental order of convergence (EOC) is widely used as an important tool for testing numerical algorithms [6,9,15,22]. The idea behind the definition of EOC is straightforward.…”

Section: Discussion On Numerical Experimentsmentioning

confidence: 99%

Numerical Simulations of a Curve Flow Related to Centro-affine Invariants

Xia,

2024

Preprint

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In this paper, we propose a novel numerical implementation for visualizing the evolutionary process of a curve flow. This flow is an invariant second-order flow in centro-affine geometry. The key of the scheme proposed in this essay is to indirectly obtain the position vector by solving the evolution equation of the support function. Additionally, to improve efficiency, we choose a specific tangential velocity which can eliminate the necessity to update normal angles at each iteration. The reason is that the normal angles do not change over time under this tangential velocity. Finally, we conduct multiple experiments to demonstrate the feasibility and efficiency of our proposed method.

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Section: Discussion On Numerical Experimentsmentioning

confidence: 99%

Numerical Simulations of a Curve Flow Related to Centro-affine Invariants

Xia,

2024

Preprint

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“…The complementary finite volume method described in [7,10] is used for the spatial discretization. The digital image can be considered as a grid of pixels denoted by V: where…”

Section: Spatial Discretization and Approximation Of Spatial Derivatimentioning

confidence: 99%

“…The first term on the right-hand side of equation 17is discretized using the firstorder upwind scheme. The second term on the right-hand side is discretized using the schemes described in detail in Appendix of [10]. Therefore, the discretization will be described only briefly in this paper.…”

Section: Spatial Discretization and Approximation Of Spatial Derivatimentioning

confidence: 99%

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Signed-distance function based non-rigid registration of image series with varying image intensity

Škardová¹,

Oberhuber²,

Tintěra

et al. 2021

Discrete &Amp; Continuous Dynamical Systems - S

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In this paper we propose a method for locally adjusted optical flow-based registration of multimodal images, which uses the segmentation of object of interest and its representation by the signed-distance function (OF dist method). We deal with non-rigid registration of the image series acquired by the Modiffied Look-Locker Inversion Recovery (MOLLI) magnetic resonance imaging sequence, which is used for a pixel-wise estimation of T 1 relaxation time. The spatial registration of the images within the series is necessary to compensate the patient's imperfect breath-holding. The evolution of intensities and a large variation of image contrast within the MOLLI image series, together with the myocardium of left ventricle (the object of interest) typically not being the most distinct object in the scene, makes the registration challenging. The paper describes all components of the proposed OF dist method and their implementation. The method is then compared to the performance of a standard mutual information maximization-based registration method, applied either to the original image (MIM) or to the signed-distance function (MIM dist). Several experiments with synthetic and real MOLLI images are carried out. On synthetic image with a single object, MIM performed the best, while OF dist and MIM dist provided better results on synthetic images with more than one object and on real images. When applied to signed-distance function of two objects of interest, MIM dist provided a larger registration error, but more homogeneously distributed, compared to OF dist. For the real MOLLI image series with left ventricle pre-segmented using a level-set method, the proposed OF dist registration performed the best, as is demonstrated visually and by measuring the increase of mutual information in the object of interest and its neighborhood.

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“…For the application of these methods within the context of image segmentation, we refer the reader to, e.g. [3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Comparison study of image segmentation techniques by curvature-driven flow of graphs

Kolář

Yazaki

2021

JSIAM Letters

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In this paper, we deal with image segmentation by a curvature-driven flow of graphs. We focus on images, where the segmented objects can be represented by 1D graphs of functions. Compared to the usual direct approach, such a description benefits from certain advantages. Three methods to image segmentation are discussed and applied in terms of the graph formulation. Then, all methods are compared in the qualitative computational study.

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Application of the Level-Set Model with Constraints in Image Segmentation

Cited by 4 publications

References 20 publications

Numerical Simulations of a Curve Flow Related to Centro-affine Invariants

Numerical Simulations of a Curve Flow Related to Centro-affine Invariants

Signed-distance function based non-rigid registration of image series with varying image intensity

Comparison study of image segmentation techniques by curvature-driven flow of graphs

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