Abstract In this work, we introduce an anisotropic minimal path model based on a new Riemannian tensor integrating the crossing-adaptive anisotropic radius-lifted tensor field and the front freezing indicator by appearance and path features. The non-local path feature only can be obtained during the geodesic distance computation process by the fast marching method. The predefined criterion derived from path feature is able to steer the front evolution by freezing the point causing high bending of the geodesic to solve the shortcut problem. We performed qualitative and quantitative experiments on synthetic and real images (including retinal vessels, rivers and roads) and compare with the minimal path models with classical anisotropic Riemannian metric and dynamic isotropic metric, which demonstrated the proposed method can detect desired targets from complex tubular tree structures.

中文翻译：

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具有快速行进前冻结方案的各向异性管状最小路径模型

摘要 在这项工作中，我们引入了一种基于新黎曼张量的各向异性最小路径模型，该模型通过外观和路径特征集成了交叉自适应各向异性半径提升张量场和前冻结指示器。非局部路径特征只能在测地距离计算过程中通过快速行进方法获得。从路径特征导出的预定义标准能够通过冻结导致测地线高度弯曲的点来引导前沿演化，从而解决捷径问题。我们对合成和真实图像（包括视网膜血管、河流和道路）进行了定性和定量实验，并与经典各向异性黎曼度量和动态各向同性度量的最小路径模型进行了比较，