IEEE Trans Image Process. 2022;31:405-418. doi: 10.1109/TIP.2021.3131940. Epub 2021 Dec 9.
Tubular structure tracking is a crucial task in the fields of computer vision and medical image analysis. The minimal paths-based approaches have exhibited their strong ability in tracing tubular structures, by which a tubular structure can be naturally modeled as a minimal geodesic path computed with a suitable geodesic metric. However, existing minimal paths-based tracing approaches still suffer from difficulties such as the shortcuts and short branches combination problems, especially when dealing with the images involving complicated tubular tree structures or background. In this paper, we introduce a new minimal paths-based model for minimally interactive tubular structure centerline extraction in conjunction with a perceptual grouping scheme. Basically, we take into account the prescribed tubular trajectories and curvature-penalized geodesic paths to seek suitable shortest paths. The proposed approach can benefit from the local smoothness prior on tubular structures and the global optimality of the used graph-based path searching scheme. Experimental results on both synthetic and real images prove that the proposed model indeed obtains outperformance comparing with the state-of-the-art minimal paths-based tubular structure tracing algorithms.
管状结构跟踪是计算机视觉和医学图像分析领域的一项关键任务。基于最小路径的方法在跟踪管状结构方面表现出了强大的能力，通过这种方法，管状结构可以自然地建模为用合适的测地线度量计算的最小测地线路径。然而，现有的基于最小路径的跟踪方法仍然存在一些困难，例如捷径和短分支组合问题，特别是在处理涉及复杂管状树结构或背景的图像时。在本文中，我们提出了一种新的基于最小路径的模型，用于结合感知分组方案进行最小交互管状结构中心线提取。基本上，我们考虑了规定的管状轨迹和曲率惩罚测地线路径来寻找合适的最短路径。所提出的方法可以受益于管状结构的局部平滑度先验和所使用的基于图的路径搜索方案的全局最优性。在合成和真实图像上的实验结果证明，与最先进的基于最小路径的管状结构跟踪算法相比，所提出的模型确实具有更好的性能。
