提出拓扑保持的图像分割方法，提升复杂结构下的分割精度。
Topology preserving Image segmentation using the iterative convolution-thresholding method
- 在迭代卷积阈值法中引入拓扑约束，确保目标连通性。
- 在复杂结构和噪声图像上分割准确率显著提升。
- 适合需要精确拓扑关系的医学影像等场景。
变分模型广泛用于图像分割，通过优化特定目标函数处理不同图像。但传统方法多关注图像视觉特征，忽视目标对象的拓扑属性，导致复杂结构图像中分割结果偏离真实情况。本文将拓扑保持约束引入迭代卷积阈值法（ICTM），提出拓扑保持的ICTM（TP-ICTM）。大量实验表明，该方法通过显式保持目标对象的连通性等拓扑特性，在具有复杂结构或噪声的图像上实现了更高的分割准确率与鲁棒性。
原文摘要 · Abstract (English)
Variational models are widely used in image segmentation, with various models designed to address different types of images by optimizing specific objective functionals. However, traditional segmentation models primarily focus on the visual attributes of the image, often neglecting the topological properties of the target objects. This limitation can lead to segmentation results that deviate from the ground truth, particularly in images with complex topological structures. In this paper, we introduce a topology-preserving constraint into the iterative convolution-thresholding method (ICTM), resulting in the topology-preserving ICTM (TP-ICTM). Extensive experiments demonstrate that, by explicitly preserving the topological properties of target objects-such as connectivity-the proposed algorithm achieves enhanced accuracy and robustness, particularly in images with intricate structures or noise.
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