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Journal of Scientific Computing

Erschienen in:

01.06.2021

Topology-Preserving 3D Image Segmentation Based on Hyperelastic Regularization

verfasst von: Daoping Zhang, Lok Ming Lui

Erschienen in: Journal of Scientific Computing | Ausgabe 3/2021

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Abstract

Image segmentation is to extract meaningful objects from a given image. For degraded images due to occlusions, obscurities or noises, the accuracy of the segmentation result can be severely affected. To alleviate this problem, prior information about the target object is usually introduced. In Chan et al. (J Math Imaging Vis 60(3):401–421, 2018), a topology-preserving registration-based segmentation model was proposed, which is restricted to segment 2D images only. In this paper, we propose a novel 3D topology-preserving registration-based segmentation model with the hyperelastic regularization, which can handle both 2D and 3D images. The existence of the solution of the proposed model is established. We also propose a converging iterative scheme to solve the proposed model. Numerical experiments have been carried out on the synthetic and real images, which demonstrate the effectiveness of our proposed model.

Vorheriger Artikel A New Sixth-Order Finite Difference WENO Scheme for Fractional Differential Equations

Nächster Artikel A Rational Approximation Scheme for Computing Mittag-Leffler Function with Discrete Elliptic Operator as Input

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Titel: Topology-Preserving 3D Image Segmentation Based on Hyperelastic Regularization
verfasst von: Daoping Zhang
Lok Ming Lui
Publikationsdatum: 01.06.2021
Verlag: Springer US
Erschienen in: Journal of Scientific Computing / Ausgabe 3/2021
Print ISSN: 0885-7474
Elektronische ISSN: 1573-7691
DOI: https://doi.org/10.1007/s10915-021-01433-y

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