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.
翻译:肿瘤结构追踪是计算机视觉和医学图像分析领域的一项关键任务。基于途径的最低限度方法在跟踪管状结构方面表现出很强的能力,通过这种方法,可以自然地将管状结构建为用适当的大地测量测量测量尺度计算出来的最低限度大地测量路径。然而,目前基于路径的最低限度追踪方法仍面临一些困难,例如捷径和短枝组合问题,特别是在处理涉及复杂的管状树结构或背景的图像时。在本文中,我们引入了一个新的基于路径的最低限度模型,用于与感知组合计划一起进行最低限度交互式管状结构中心提取。基本上,我们考虑到规定的管状轨和弯曲分化的大地测量路径,以寻找合适的最短路径。拟议的方法可以得益于管状结构之前的当地光滑以及用图式路径搜索方法的全球最佳性。合成图和真实图像的实验结果都证明,拟议的模型确实取得了与基于光谱的最起码路径的管状结构跟踪算法的超效。