基于超弹性正则化的保拓扑三维图像分割
Topology-Preserving 3D Image Segmentation Based On Hyperelastic Regularization
摘要：
图像分割是从给定的图像中提取有意义的目标。对于由于遮挡、模糊或噪声引起的退化图像，分割结果的准确性会受到严重影响。为了缓解这个问题，通常引入关于目标对象的先验信息。在[10]中，提出了一种拓扑保持的基于配准的分割模型，该模型仅限于分割2D图像。在本文中，我们提出了一种新的三维拓扑保持配准的分割模型与超弹性正则化，它可以处理二维和三维图像。建立了该模型解的存在性。我们还提出了一个收敛的迭代方案来解决所提出的模型。对合成图像和真实图像进行了数值实验，实验结果表明了该模型的有效性。
英文摘要：
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 [10], 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
